Assessing the condition of a joint and devising treatment

ABSTRACT

Methods are disclosed for assessing the condition of a cartilage in a joint, particularly a human knee. The methods include converting an image such as an MRI to a three dimensional map of the cartilage. The cartilage map can be correlated to a movement pattern of the joint to assess the affect of movement on cartilage wear. Changes in the thickness of cartilage over time can be determined so that therapies can be provided. Information on thickness of cartilage and curvature of cartilage or subchondral bone can be used to plan therapy. Information on movement pattern can be used to plan therapy.

[0001] This application claims the benefit of U.S. ProvisionalApplication Serial No. 60/112,989, filed Dec. 16, 1998 and is acontinuation of PCT/US99/30265, filed Dec. 16, 1999, both of which areherein incorporated by reference.

[0002] This invention was supported in part by a National Institute ofHealth Grant No. PAR-97-014, and the government may have rights in thisinvention.

BACKGROUND OF THE INVENTION

[0003] 1. Field of Invention

[0004] This invention relates to assessing the condition of a joint andthe use of the assessment in aiding in prevention of damage to the jointor treatment of diseased cartilage in the joint.

[0005] 2. Background

[0006] Osteoarthritis is the most common condition to affect humanjoints as well as a frequent cause of locomotor pain and disability.More particularly, osteoarthritis (OA) of the knee occurs in asubstantial portion of the population over the age of fifty.

[0007] In spite of its societal impact and prevalence, however, there isa paucity of information on the factors that cause osteoarthritis toprogress more rapidly in some individuals and not in others. Previouslyconsidered a “wear and tear” degenerative disease with littleopportunity for therapeutic intervention, osteoarthritis is nowincreasingly viewed as a dynamic process with potential for newpharmacologic and surgical treatment modalites such as cartilagetransplantation, osteochondral allo- or autografting, osteotomies andtibial corticotomies with angular distraction.

[0008] However, the appropriate deployment and selection of treatmentinterventions for OA is dependent on the development of better methodsfor the assessment of the condition of a patient's joint and thedegeneration process.

[0009] There is, therefore, a need for improved methods for examiningthe factors that influence as well as quantification of the progressionof the disease.

[0010] Magnetic resonance imaging (MRI) is an accurate non-invasiveimaging technique for visualization of articular cartilage inosteoarthritis, particularly in knees. However, current MRI techniquescannot provide information on the relationship between the location ofthe cartilage loss and variations in the load bearing areas during thewalking cycle. This information is important since it has been shownthat dynamic loads during walking are related to the progression of kneeOA. Thus, the ability to locate cartilage defects or areas of cartilagethinning relative to the load bearing areas of the knee could bevaluable in evaluating factors influencing the progression ofosteoarthritis.

[0011] References

[0012] 1. Alexander E J: Estimating the motion of bones from markers onthe skin [Doctoral Dissertation]. University of Illinois at Chicago;1998.

[0013] 2. Alexander E J, Andriacchi T P: Correcting for deformation inskin-based marker systems. Proceedings of the 3rd Annual Gait andClinical Movement Analysis Meeting, San Diego, Calif., 1998.

[0014] 3. Alexander E J, Andriacchi T P: Internal to externalcorrespondence in the analysis of lower limb bone motion. Proceedings ofthe 1999 ASME Summer Bioengineering Conference, Big Sky, Mont., 1999.

[0015] 4. Alexander E J, Andriacchi T P: State estimation theory inhuman movement analysis. Proceedings of the 1998 ASME InternationalMechanical Engineering Congress, 1998.

[0016] 5. Alexander E J, Andriacchi T P, Lang P K: Dynamic functionalimaging of the musculoskeletal system. ASME Winter InternationalCongress and Exposition, Nashville, Tenn., 1999.

[0017] 6. Alexander E J, Andriacchi T P, Naylor D L: Optimizationtechniques for skin deformation correction. International Symposium on3-D Human Movement Conference, Chattanooga, Tenn., 1998.

[0018] 7. Allen P R, Denham R A, Swan A V: Late degenerative changesafter meniscectomy: factors affecting the knee after operations. J BoneJoint Surg 1984; 66B: 666-671.

[0019] 8. Alley M T, Shifrin R Y, Pelc N J, Herfkens RJ: Ultrafastcontrast-enhanced three dimensional MR angiography: state of the art.Radiographics 1998; 18: 273-285.

[0020] 9. Andriacchi T P: Dynamics of knee malalignment. Orthop ClinNorth Am 1994; 25: 395-403.

[0021] 10. Andriacchi T P, Alexander E J, Toney M K, Dyrby C O, Sum J: Apoint cluster method for in vivo motion analysis: applied to a study ofknee kinematics. J Biomech Eng 1998; 120(12): 743-749.

[0022] 11. Andriacchi T P, Lang P, Alexander E, Hurwitz D: Methods forevaluating the progression of osteoarthritis. J Rehab Res Develop 2000;37, 2: 163-170.

[0023] 12. Andriacchi T P, Sen K, Toney M K, Yoder D: New developmentsin musculoskeletal testing. Proceedings of the Canadian Society ofBiomechanics, 1994.

[0024] 13. Andriacchi T P, Strickland A B: Gait analysis as a tool toassess joint kinetics biomechanics of normal and pathological humanarticulating joints. Nijhoff, Series E 1985; 93: 83-102.

[0025] 14. Andriacchi T P, Toney M K: In vivo measurement ofsix-degrees-of-freedom knee movement during functional testing.Transactions of the Orthopedic Research Society 1995: 698.

[0026] 15. Beaulieu C F, Hodge D K, Bergman A G: Glenohumeralrelationships during physiological shoulder motion and stress testing:initial experience with open MRI and active scan-plane registration.Radiology 1999: accepted for publication.

[0027] 16. Beaulieu C F, Hodge D K, Thabit G, Lang P K, Bergman A G:Dynamic imaging of glenohumeral instability with open MRI. Int. Societyfor Magnetic Resonance in Medicine, Sydney, Australia, 1998.

[0028] 17. Benedetti M G, Cappozzo A: Anatomical landmark definition andidentification in computer aided movement analysis in a rehabilitationcontext II (Internal Report). U Degli Studi La Sapienza 1994: 1-31.

[0029] 18. Bergman A G, Beaulieu C F, Pearle A D, et al.: Joint motion:assessment by upright interactive dynamic near-real time MR imaging.Radiological Society of North America, 83rd Scientific Assembly andAnnual Meeting, Chicago, Ill., 1997.

[0030] 19. Biswal S, Hastie T, Andriacchi T, Bergman G, Dillingham M F,Lang P: The rate of progressive cartilage loss at the knee is dependenton the location of the lesion: a longitudinal MRI study in 43 patients.Arthritis&Rheumatism 2000: submitted for publication.

[0031] 20. Bobic V: Arthoscopic osteochondral autograft transplantationin anterior cruciate ligament reconstruction: a preliminary clinicalstudy. Knee Surg Sports Traumatol Arthrosc 1996; 3 (4): 262-264.

[0032] 21. Boe S, Hansen H: Arthroscopic partial meniscectomy inpatients aged over 50. J Bone Joint Surg 1986; 68B: 707.

[0033] 22. Bregler. C, Hertzmann A, Biermann H: Recovering non-rigid 3Dshape from image streams. Proc. IEEE Conference on Computer Vision andPattern Recognition 2000: in press.

[0034] 23. Brittberg M, Lindahl A, Homminga G, Nilsson A, Isaksson 0,Peterson L: A critical analysis of cartilage repair. Acta Orthop Scand1997; 68 (2)186-191.

[0035] 24. Brittberg M, Lindahl A, Nilsson A, Ohlsson C, Isaksson 0,Peterson L: Treatment of deep cartilage defects in the knee withautologous chondrocyte transplantation. N Engl J Med 1994; 331 (14):889-895.

[0036] 25. Broderick L S, Turner D A, Renfrew D L, Schnitzer T J, Huff JP, Harris C: Severity of articular cartilage abnormality in patientswith osteoarthritis: evaluation with fast spin-echo MR vs arthroscopy.AJR 1994; 162: 99-103.

[0037] 26. Butts K, Pauly J M, Kerr A B, Bergman A G, Beaulieu C F:Real-Time MR imaging of joint motion on an open MR imaging scanner.Radiological Society of North America, 83rd Scientific Assembly andAnnual Meeting, Chicago, Ill., 1997.

[0038] 27. Cohen Z A, McCarthy D M, Kwak, S D, Legrand P, Fogarasi F,Ciaccio E J, Ateshian G A:

[0039] Knee cartilage topography, thickness, and contact areas from MRI:in-vitro calibration and in-vivo measurements. Osteoarthritis andCartilage 1999; 7: 95-109.

[0040] 28. Daniel B, Butts K, Glover G, Herfkens R: Breast cancer:gadolinium-enhanced MR imaging with a 0.5T open imager and three-pointDixon technique. Radiology 1998; 207 (1): 183-190.

[0041] 29. Disler D G: Fat-suppressed three-dimensional spoiledgradient-recalled MR imaging:

[0042] assessment of articular and physeal hyaline cartilage. AJR 1997;169: 1117-1123.

[0043] 30. Disler D G, McCauley T R, Kelman C G, et al.: Fat-suppressedthree-dimensional spoiled gradient-echo MR imaging of hyaline cartilagedefects in the knee: comparison with standard MR imaging andarthroscopy. AJR 1996; 167: 127-132.

[0044] 31. Disler D G, McCauley T R, Wirth C R, Fuchs M D: Detection ofknee hyaline cartilage defects using fat-suppressed three-dimensnionalspoiled gradient-echo MR imaging:

[0045] comparison with standard MR imaging and correlation witharthrosocpy. AJR 1995; 165: 377-382.

[0046] 32. Doherty M, Hutton C, Bayliss M T: Osteoarthritis. In:Maddison P J, Isenberg D A, Woo P, et al., eds. Oxford Textbook ofRheumatology, vol 1. Oxford, New York, Tokyo: Oxford University Press,1993; 959-983.

[0047] 33. Dougados M, Gueguen A, Nguyen M, et al.: Longitudinalradiologic evaluation of osteoarthritis of the knee. J Rheumatol 1992;19: 378-384.

[0048] 34. Du Y P, Parker D L, Davis W L: Vessel enhancement filteringin three-dimensional MR angiography. J Magn Res Imaging 1995; 5:151-157.

[0049] 35. Du Y P, Parker D L, Davis W L, Cao G: Reduction ofpartial-volume artifacts with zero-filled interpolation inthree-dimensional MR angiography. J Magn Res Imaging 1994; 4: 733-741.

[0050] 36. Dumoulin C L, Souza S P, Darrow R D: Real-time positionmonitoring of invasive devices using magnetic resonance. Magn Reson Med1993; 29: 411-5.

[0051] 37. Dyrby C O: The three-dimensional kinematics of knee jointmotion: fimctional differences in two populations [Master's Thesis].University of Illinois at Chicago; 1998.

[0052] 38. Eckstein F, Westhoff J, Sittek H, et al.: In vivoreproducibility of three-dimensional cartilage volume and thicknessmeasurements with MR imaging. AJR 1998; 170(3): 593-597.

[0053] 39. Elting J J, Hubbell J C: Unilateral frame distraction:proximal tibial valgus osteotomy for medial gonarthritis. Contemp Orthop1993; 27(6): 522-524.

[0054] 40. Falcao A X, Udupa J K, Samarasekera S, Sharma S: User-steeredimage segmentation paradigms: Live wire and live lane. Graphical Modelsand Image Processing 1998; 60: 233-260.

[0055] 41. Felson D T, Zhang Y, Anthony J M, Naimark A, Anderson J J:Weight loss reduces the risk for symptomatic knee osteoarthritis inwomen: the Framingham study. Ann Intern Med 1992; 116: 535-539.

[0056] 42. Garrett J C: Osteochondral allografts for reconstruction ofarticular defects of the knee. Instr Course Lect 1998; 47: 517-522.

[0057] 43. Ghosh S, Newitt D C, Majumdar S: Watershed segmentation ofhigh resolution articular cartilage image. International Society forMagnetic Resonance in Medicine, Philadelphia, 1999.

[0058] 44. Gouraud H: Continuous shading of curved surfaces. IEEE Transon Computers 1971; C-20(6).

[0059] 45. Gray A: Modern Differential Geometry of Curves and Surfaces.1993: CRC Press, Inc.

[0060] 46. Hargreaves B A, Gold G E, Conolly S M, Nishimura D G:Technical considerations for DEFT imaging. International Society forMagnetic Resonance in Medicine, Sydney, Australia, April 17-24, 1998.

[0061] 47. Hargreaves B A, Gold G E, Lang P K, Bergman G, Conolly S M,Nishimura D G: Imaging of articular cartilage using driven equilibrium.International Society for Magnetic Resonane in Medicine, Sydney,Australia, Apr. 17-24, 1998.

[0062] 48. Hayes C, Conway W: Evaluation of articular cartilage:radiographic and cross-sectional imaging techniques. Radiographics 1992;12: 409428.

[0063] 49. Henkelman R M, Stanisz G, Kim J, Bronskill M: Anisotropy ofNMR properties of tissues. Magn Res Med 1994; 32: 592-601.

[0064] 50. Hoppenfeld S, Huton R: Physical Examination of the Knee. In:Hoppenfeld S, ed.

[0065] Physical Examination of the Spine and Extremities:Appleton-Century-Crofts/Prentice-Hall, 1976; 171-196.

[0066] 51. Hyhlik-Durr A, Faber S, Burgkart R, et al.: Precision oftibial cartilage morphometry with a coronal water-excitation MRsequence. European Radiology 2000; 10 (2): 297-303.

[0067] 52. Irarrazabal P, Nishimura D G: Fast three-dimensional magneticresonance imaging. Mag Res Med 1995; 33: 656-662.

[0068] 53. Johnson F, Leitl S, Waugh W: The distribution of load acrossthe knee. A comparison of static and dynamic measurements. J Bone JointSurg 1980; 62B: 346-349.

[0069] 54. Johnson T S: In vivo contact kinematics of the knee joint:Advancing the point cluster technique. Ph.D. thesis, University ofMinnesota 1999.

[0070] 55. Johnson T S, Andriacchi T P, Laurent M: Development of a kneewear method based on prosthetic in vivo slip velocity. Transactions ofthe Orthopedic Research Society, 46th Annual Meeting, March, 2000.

[0071] 56. LaFortune M A, Cavanagh P R, Sommer H J, Kalenak A: Threedimensional kinematics of the human knee during walking. J. Biomechanics1992; 25: 347-357.

[0072] 57. Lang P, Alexander E, Andriacchi T: Funcional joint imaging: anew technique integrating MRI and biomotion studies. InternationalSociety for Magnetic Resonance in Medicine, Denver, Apr. 18, 2000-Apr.24, 2000, 2000.

[0073] 58. Lang P, Biswal S, Dillingham M, Bergman G, Hastie T,Andriacchi T: Risk factors for progression of cartilage loss: alongitudinal MRI study. European Society of Musculoskeletal Radiology,6th Annual Meeting, Edinburgh, Scotland, 1999.

[0074] 59. Lang P, Hargreaves B A, Gold G, et al.: Cartilage imaging:comparison of driven equilibrium with gradient-echo, SPGR, and fastspin-echo sequences. International Society for Magnetic Resonance inMedicine, Sydney, Australia, Apr. 17-24, 1998.

[0075] 60. Ledingham J, Regan M, Jones A, Doherty. M: Factors affectingradiographic progression of knee osteoarthritis. Ann Rheum Dis 1995; 54:53-58.

[0076] 61. Lorensen W E, Cline H E: Marching cubes: a high resolution 3dsurface construction algorithm. Comput Graph 1987; 21: 163-169.

[0077] 62. Losch A, Eckstein F, Haubner M, Englmeier KH: A non-invasivetechnique for 3-dimensional assessment of articular cartilage thicknessbased on MRI part 1: development of a computational method. Magn ResImaging 1997; 15, 7: 795-804.

[0078] 63. Lu T W, O'Connor J J: Bone position estimation from skinmarker co-ordinates using globals optimisation with joint constraints. JBiomechanics 1999; 32: 129-134.

[0079] 64. Lucchetti L, Cappozzo A, Cappello A, Della Croce U: Skinmovement artefact assessment and compensation in the estimation ofknee-joint kinematics. J Biomechanics 1998; 31: 977-984.

[0080] 65. Lynch J A, Zaim S, Zhao J, Stork A, Genant H K: Cartilagesegmentation of 3D MRI scans of the osteoarritic knee combining userknowledge and active contours. Proc. SPIE 3979 Medical Imaging, SanDiego, February 2000.

[0081] 66. Maki J H, Johnson G A, Cofer G P, MacFall J R: SNRimprovement in NMR microscopy using DEFT. J Mag Res 1988.

[0082] 67. Meyer C H, Pauly J M, Macovski A, Nishimura DG: Simultaneousspatial and spectral selective excitation. Magn Res Med 1990; 15:287-304.

[0083] 68. Mollica Q, Leonardi W, Longo G, Travaglianti G: Surgicaltreatment of arthritic varus knee by tibial corticotomy and angulardistraction with an external fixator. Ital J Orthop Traumatol 1992; 18(1): 17-23.

[0084] 69. Nizard RS: Role of tibial osteotomy in the treatment ofmedial femorotibial osteoarthritis. Rev Rhum Engl Ed 1998; 65 (7-9):443-446.

[0085] 70. Noll D C, Nishimura D, Macovski A: Homodyne detection inmagnetic resonance imaging. IEEE Trans Med Imag10 1991; 10 (2): 154-163.

[0086] 71. Ogilvie-Harris D J, Fitsialos D P: Arthroscopic management ofthe degenerative knee. Arthroscopy 1991; 7: 151-157.

[0087] 72. Pearle A, Bergman A G, Daniels B, et al.: Use of an externalMR-tracking coil for active scan plane registration during dynamicmusculoskeletal MR imaging in a vertically open MRT unit. AmericanRoentgen Ray Society, San Francisco, Calif., 1998.

[0088] 73. Pearle A D, Daniel B L, Bergman A G: Joint motion in an openMR unit using MR tracking. JMRI 1999; 10 (10): 1566-1576.

[0089] 74. Peterfy C, van Dijke C, Lu Y, et al.: Quantification of thevolume of articular cartilage in the metacarpophalangeal joints of thehand: accuracy and precision of three-dimensional MR imaging. AJR 1995;165: 371-375.

[0090] 75. Peterfy C G, Majumdar S, Lang P, van Dijke C, Sack K, GenantH K: MR imaging of the arthritic knee: improved discrimination ofcartilage, synovium, and effusion with pulsed saturation transfer andfat-suppressed T1-weighted sequences. Radiology 1994; 191(2): 413-419.

[0091] 76. Peterfy C G, van Dijke C F, Janzen D L, et al.:Quantification of articular cartilage in the knee with pulsed saturationtransfer subtraction and fat-suppressed MR imaging: optimization andvalidation. Radiology 1994; 192(2): 485-491.

[0092] 77. Piplani M A, Disler D G, McCauley T R, Holmes T J, Cousins JP: Articular cartilage volume in the knee: semiautomated determinationfrom three-dimensional reformations of MR images. Radiology 1996; 198:855-859.

[0093] 78. Potter H G, Linklater J M, Allen A A, Hannafm J A, Haas S B:Magnetic resonance imaging of articular cartilage in the knee: anevaluation with use of fast-spin-echo imaging. J Bone Joint Surg 1998;80-A(9): 1276-1284.

[0094] 79. Prodromos C C, Andriacchi T P, Galante J O: A relationshipbetween gait and clinical changes following high tibial osteotomy. JBone Joint Surg 1985; 67A: 1188-1194.

[0095] 80. Radin E L, Burr D B, Caterson B, Fyhrie D, Brown T D, Boyd RD: Mechanical determinants of osteoarthrosis. Sem Arthr Rheum 1991;21(3): 12-21.

[0096] 81. Radin E L, Burr D B, Fyhrie D: Characteristics of jointloading as it applies to osteoarthrosis. In: Mow VC, Woo S-Y, RatcliffeT, eds. Symposium on Biomechanics of Diarthrodial Joints, vol 2. NewYork, N.Y.: Springer-Verlag, 1990; 437-451.

[0097] 82. Recht M P, Piraino D W, Paletta G A, Schils J P, Belhobek GH: Accuracy of fat-suppressed three-dimensional spoiled gradient-echoFLASH MR imaging in the detection of patellofemoral articular cartilageabnormalities. Radiology 1996; 198: 209-212.

[0098] 83. Recht M P, Resnick D: MR imaging of articular cartilage:current status and future directions. AJR 1994; 163: 283-290.

[0099] 84. Ritter M A, Faris P M, Keating E M, Meding J B: Postoperativealignment of total knee replacement. Clin Orthop 1994; 299: 153-156.

[0100] 85. Saito T, Toriwaki J-I: New algorithms for Euclidean distancetransformation of an n-dimensional digitized picture with applications.Pattern Recognition 1994; 27 (11): 1551-1565.

[0101] 86. Schipplein O D, Andriacchi T P: Interaction between activeand passive knee stabilizers during level walking. J Orthop Res 1991; 9:113-119.

[0102] 87. Schouten JSAG, van den Ouweland F A, Valkenburg H A: A 12year follow up study in the general population on prognostic factors ofcartilage loss in osteoarthritis of the knee. Ann Rheum Dis 1992; 51:932-937.

[0103] 88. Sharif M, George E, Shepstone L, et al.: Serum hyaluronicacid level as a predictor of disease progression in osteoarhritis of theknee. Arthritis Rheum 1995; 38: 760-767.

[0104] 89. Sharma L, D. E. H, Thonar EJMA, et al.: Knee adductionmoment, serum hyaluronic acid level, and disease severity in medialtibiofemoral osteoarthritis. Arthritis and Rheumatism 1998; 41(7):1233-40.

[0105] 90. Shoup R R, Becker E D: The driven equilibrium Fouriertransform NMR technique: an experimental study. J Mag Res 1972; 8.

[0106] 91. Slemenda C, Mazzuca S, Brandt K, Katz B: Lower extremity leantissue mass and strength predict increases in pain and in functionalimpairment in knee osteoarthritis. Arthritis Rheum 1996; 39(suppl):S212.

[0107] 92. Slemenda C, Mazzuca S, Brandt K, Katz B: Lower extremitystrength, lean tissue mass and bone density in progression of kneeosteoarthritis. Arthritis Rheum 1996; 39(suppl): S169.

[0108] 93. Solloway S, Hutchinson C E, Waterton J C, Taylor C J: The useof active shape models for making thickness measurements of articularcartilage from MR images. Mag Res Med 1997; 37:943-952.

[0109] 94. Spoor C W, Veldpas F E: Rigid body motion calculated fromspatial coordinates of markers. J Biomechanics 1980; 13: 391-393.

[0110] 95. Stamrnmberger T, Eckstein F, Englmeier K H, Reiser M:Determination of 3D cartilage thickness data from MR imaging:computational method and reproducibility in the living. Mag Res Med1999; 41: 529-536.

[0111] 96. Stammberger T, Eckstein F, Michaelis M, Englmeier K H, ReiserM: Interobserver reproducibility of quantitative cartilage measurements:Comparison of B-spline snakes and manual segmentation. Mag Res Imaging1999; 17:1033-1042.

[0112] 97. Steines D, Berger F, Cheng C, Napel S, Lang P: 3D thicknessmaps of articular cartilage for quantitative assessment ofosteoarthritis. To be presented at ACR 64th Annual Scientific Meeting,Philadelphia, October 2000.

[0113] 98. Steines D, Cheng C, Wong A, Berger F, Napel S, Lang P:Segmentation of osteoarthritic femoral cartilage from MR images.CARS—Computer-Assisted Radiology and Surgery, p. 578-583, San Francisco,2000.

[0114] 99. Steines D, Napel S, Lang P: Measuring volume of articularcartilage defects in osteoarthritis using MRI. To be presented at ACR64th Annual Scientific Meeting, Philadelphia, October 2000.

[0115] 100. Stevenson S, Dannucci G A, Sharkey N A, Pool R R: The fateof articular cartilage after transplantation of fresh and cryopreservedtissue-antigen-matched and mismatched osteochondral allografts in dogs.J Bone Joint Surg 1989; 71 (9): 1297-1307.

[0116] 101. Tieschky M, Faber S, Haubner M, et al.: Repeatability ofpatellar. cartilage thickness patterns in the living, using afat-suppressed magnetic resonance imaging sequence with shortacquisition time and three-dimensional data processing. J Orthop Res1997; 15(6): 808-813.

[0117] 102. Tomasi C, Kanade T: Shape and motion from image streamsunder orthography—a factorization method. Proc Nat Acad Sci 1993;90(21): 9795-9802.

[0118] 103. Tsai J, Ashjaee S, Adalsteinsson E, et al.: Application of aflexible loop-gap resonator for MR imaging of articular cartilage at3.0T. International Society for Magnetic Resonance in Medicine, Denver,Apr. 18, 2000-Apr. 24, 2000, 2000.

[0119] 104. Wang J W, Kuo K N, Andriacchi T P, Galante J O: Theinfluence of walking mechanics and time on the results of proximaltibial osteotomy. J Bone Joint Surg 1990; 72A: 905-909.

[0120] 105. Waterton J C, Solloway S, Foster J E, Keen M C, Gandy S,Middleton B J, Maciewicz R A, Watt I, Dieppe P A, Taylor C J: Diurnalvariation in the femoral articular cartilage of the knee in young adulthumans. Mag Res Med 2000, 43: 126-132.

[0121] 106. Woolf S D, Chesnick F, Frank J, Lim K, Balaban R:Magnetization transfer contrast: MR imaging of the knee. Radiology 1991;179: 623-628.

[0122] 107. Worring M, Smeulders A W M: Digital curvature estimation.CVGIP: Image Understanding, 1993. 58(3): p. 366-382.

[0123] 108. Yan C H: Measuring changes in local volumetric bone density:new approaches to quantitative computed tomography, Ph.D. thesis, 1998,Dept. of Electrical Engineering, Stanford University

[0124] 109. Yao L, Gentili A, Thomas A: Incidental magnetizationtransfer contrast in fast spin-echo imaging of cartilage. J Magn ResonImaging 1996; 6 (1): 180-184.

[0125] 110. Yao L, Sinha S, Seeger L: MR imaging of joints: analyticoptimization of GRE techniques at 1.5 T. AJR 1992; 158(2): 339-345.

[0126] 111. Yasuda K, T. M, Tsuchida T, Kameda K: A 10 to 15 year followup observation of high tibial osteotomy in medial compartmentosteoarthritis. Clin Orthop 1992; 282: 186-195.

[0127] 112. Kass M, Witkin A, Terzopoulos D: Snakes: Active contourmodels. Int J Comput Vision 1988; 1:321-331

[0128] 113. Falcao A X, Udupa J K, Samarasekera S, Sharma S, Hirsch B E,Lotufo R A: User-steered image segmentation paradigms: Live wire andlive lane. GMIP 1998; 60, 233-260

[0129] 114. Steines, D., et al., Segmentation of osteoarthritic femoralcartilage using live wire, ISMRM Eight Scientific Meeting, Denver Colo.,2000

SUMMARY OF THE INVENTION

[0130] This invention relates to assessing the condition of a joint of amammal, particularly a human subject, using the assessment to treat andmonitor the subject as needed for cartilage degeneration problems. Whilethe numerous aspects of the invention are useful for joints generally,they are particularly suited for dealing with the human knee. Someaspects related the static images and degeneration patterns of acartilage, while others relate to the interaction of such images andpatterns to provide a better means of assessing the condition of acartilage. One aspect of this invention is a method for assessing thecondition of a cartilage. The method comprises obtaining an image of acartilage, (preferably a magnetic resonance image), converting the imageto a three-dimensional degeneration pattern, and evaluating the degreeof degeneration in a volume of interest of the cartilage. By performingthis method at an initial time T, and a later time T₂, one can determinethe change in the volume of interest and evaluate what steps to take fortreatment.

[0131] Another aspect of this invention is a method of estimating theloss of cartilage in a joint. The method comprises obtaining athree-dimensional map of the cartilage at an initial time andcalculating the thickness or regional volume of a region thought tocontain degenerated cartilage so mapped at the initial time, obtaining athree-dimensional map of the cartilage at a later time, and calculatingthe thickness or regional volume of the region thought to containdegenerated cartilage so mapped at the later time, and determining theloss in thickness or regional volume of the cartilage between the laterand initial times. The 3D map may be a thickness map, a biochemical mapor a combination.

[0132] Another aspect of the invention is a method for assessing thecondition of cartilage in a joint of a human, which method compriseselectronically transferring an electronically-generated image of acartilage of the joint from a transferring device to a receiving devicelocated distant from the transferring device; receiving the transferredimage at the distant location; converting the transferred image to adegeneration pattern of the cartilage; and transmitting the degenerationpattern to a site for analysis.

[0133] Another aspect of the invention is a method for determining thevolume of cartilage loss in a region of a cartilage defect of acartilage in joint of a mammal. The method comprises (a) determining thethickness, D_(N), of the normal cartilage near the cartilage defect; (b)obtaining the thickness of the cartilage defect, D_(D), of the region;(c) subtracting D_(D) from D_(N) to give the thickness of the cartilageloss, D_(L); and (d) multiplying the D_(L) value times the area of thecartilage defect, A_(D), to give the volume of cartilage loss.

[0134] Still another aspect of the invention is a method of estimatingthe change of a region of cartilage in a joint of a mammal over time.The method comprises (a) estimating the width or area or volume of aregion of cartilage at an initial time T₁, (b) estimating the width orarea or volume of the region of cartilage at a later time T₂, and (c)determining the change in the width or area or volume of the region ofcartilage between the initial and the later times.

[0135] Still another aspect of the invention is a method of estimatingthe loss of cartilage in a joint. The method comprises (a) defining a 3Dobject coordinate system of the joint at an initial time, T₁; (b)identifying a region of a cartilage defect within the 3D objectcoordinate system; (c) defining a volume of interest around the regionof the cartilage defect whereby the volume of interest is larger thanthe region of cartilage defect, but does not encompass the entirearticular cartilage; (d) defining the 3D object coordinate system of thejoint at a second timepoint, T₂; (e) placing the identically-sizedvolume of interest into the 3D object coordinate system at timepoint T₂using the object coordinates of the volume of interest at timepoint T₁;(f) and measuring any differences in cartilage volume within the volumeof interest between timepoints T₁, and T₂.

[0136] Another aspect of this invention is a method for providing abiochemically-based map of joint cartilage. The method comprisesmeasuring a detectable biochemical component throughout the cartilage,determining the relative amounts of the biochemical component throughoutthe cartilage; mapping the amounts of the biochemical component throughthe cartilage; and determining the areas of cartilage deficit byidentifying the areas having an altered amount of the biochemicalcomponent present.

[0137] Once a map is obtained, it can be used in assessing the conditionof a cartilage at an initial time and over a time period. Thus, thebiochemical map may be used in the method aspects of the invention in amanner similar to the cartilage thickness map.

[0138] Another aspect of this invention is a method for assessing thecondition of cartilage in a joint from a distant location. The methodcomprises electronically transferring an electronically-generated imageof a cartilage of the joint from a transferring device to a receivingdevice located distant from the transferring device; receiving thetransferred image at the distant location; converting the transferredimage to a degeneration pattern of the cartilage; and transmitting thedegeneration pattern to a site for analysis.

[0139] Another aspect of the invention is a kit for aiding in assessingthe condition of cartilage in a joint of a mammal, which kit comprises asoftware program, which when installed and executed on a computer readsa cartilage degeneration pattern presented in a standard graphics formatand produces a computer readout showing a cartilage thickness map of thedegenerated cartilage.

[0140] Another aspect of this invention is a method for assessing thecondition of a subject's cartilage in a joint, the method comprisesobtaining a three dimensional biochemical representation of thecartilage, obtaining a morphological representation of the cartilage,and merging the two representations, and simultaneously displaying themerged representations on a medium. The merged representations are thenused to assess the condition of a cartilage, estimate the loss ofcartilage in a joint, determining the volume of cartilage loss in aregion of cartilage defect, or estimating the change of a region ofcartilage at a particular point in time or over a period of time.

[0141] A method for correlating cartilage image data, bone image data,and opto-electrical image data for the assessment of the condition of ajoint, which method comprises (a) obtaining the bone image data of thejoint with a set of skin reference markers positioned in externally nearthe joint, (b) obtaining the opto-electrical image data of the jointwith a set of skin reference markers positioned in the same manner as(a), and (c) using the skin reference markers to correlate the imagesobtained in (a) and (b) with each other, wherein each skin referencemarker is detectable in the bone data and the opto-electrical data. Themethod also can be used to further evaluate cartilage image data that isobtained using a similarly positioned set of skin reference markers.

[0142] Another aspect of the invention is a skin reference marker thatcomprises (a) a material detectable by an imaging technique; (b) acontainer for holding the material, (c) a material that causes thecontainer to adhere to the skin of a human, and (d) a reflectivematerial placed on the surface of the container.

[0143] Another aspect of the invention is a biochemical map of acartilage that comprises a three-dimensional representation of thedistribution of the amount of the biochemical component throughout thecartilage.

[0144] Another aspect of the invention is a method for providing abiochemically-based map of joint cartilage of a mammal, wherein thejoint comprises cartilage and associated bones on either side of thejoint, which method comprises (a) measuring a detectable biochemicalcomponent throughout the cartilage; (b) determining the relative amountsof the biochemical component throughout the cartilage; (c) mapping theamounts of the biochemical component in three dimensions through thecartilage; and (d) determining the areas of abnormal joint cartilage byidentifying the areas having altered amounts of the biochemicalcomponent present.

[0145] Another aspect of the invention is a method for deriving themotion of bones about a joint from markers placed on the skin, whichmethod comprises (a) placing at least three external markers on thepatient's limb segments surrounding the joint, (b) registering thelocation of each marker on the patient's limb while the patient isstanding completely still and while moving the limb, (c) calculating theprincipal axis, principal moments and deformation of rigidity of thecluster of markers, and (d) calculating a correction to the artifactinduced by the motion of the skin markers relative to the underlyingbone.

[0146] Another aspect of the invention is a system for assessing thecondition of cartilage in a joint of a human, which system comprises (a)a device for electronically transferring a cartilage degenerationpattern for the joint to a receiving device located distant from thetransferring device; (b) a device for receiving the cartilagedegeneration pattern at the remote location; (c) a database accessibleat the remote location for generating a movement pattern for the jointof the human wherein the database includes a collection of movementpatterns of human joints, which patterns are organized and can beaccessed by reference to characteristics such as type of joint, gender,age, height, weight, bone size, type of movement, and distance ofmovement; (d) a device for generating a movement pattern that mostclosely approximates a movement pattern for the human patient based onthe characteristics of the human patient; (e) a device for correlatingthe movement pattern with the cartilage degeneration pattern; and (f) adevice for transmitting the correlated movement pattern with thecartilage degeneration pattern back to the source of the cartilagedegeneration pattern.

[0147] A method for assessing the condition of the knee joint of a humanpatient, wherein the knee joint comprises cartilage and associated boneson either side of the joint, which method comprises (a) obtaining thepatient's magnetic resonance imaging (MRI) data of the knee showing atleast the bones on either side of the joint, (b) segmenting the MRI datafrom step (a), (c) generating a geometrical representation of the boneof the joint from the segmented MRI data, (d) assessing the patient'sgait to determine the load pattern or the cartilage contact pattern ofthe articular cartilage in the joint during the gait assessment, and (e)correlating the load pattern or cartilage contact pattern obtained instep (d) with the geometrical representation obtained in step (c).

[0148] Another aspect of the invention is a method of assessing the rateof degeneration of cartilage in the joint of a mammal, wherein the jointcomprises cartilage and the bones on either side of the cartilage, whichmethod comprises. (a) obtaining a cartilage degeneration pattern of thejoint that shows an area of greater than normal degeneration, (b)obtaining a movement pattern of the joint that shows where the opposingcartilage surfaces contact, (c) comparing the cartilage degenerationpattern with the movement pattern of the joint, and (d) determining ifthe movement pattern shows contact of one cartilage surface with aportion of the opposing cartilage surface showing greater than normaldegeneration in the cartilage degeneration pattern.

[0149] Another aspect of the invention is a method for monitoring thetreatment of a degenerative joint condition in a mammal, wherein thejoint comprises cartilage and accompanying bones on either side of thejoint, which method comprises (a) comparing the movement pattern of thejoint with the cartilage degeneration pattern of the joint; (b)determining the relationship between the movement pattern and thecartilage degeneration pattern; (c) treating the mammal to minimizefurther degeneration of the joint condition; and (d) monitoring thetreatment to the mammal.

[0150] Still another aspect of the invention is a method of assessingthe condition of a joint in a mammal, wherein the joint comprisescartilage and accompanying bones on either side of the joint, whichmethod comprises (a) comparing the movement pattern of the joint withthe cartilage degeneration pattern of the joint; and (b) determining therelationship between the movement pattern and the cartilage degenerationpattern

[0151] Other aspects of the invention may be apparent upon furtherreading the specification and claims of the patent application.

BRIEF DESCRIPTION OF THE DRAWINGS

[0152] In the accompanying drawings:

[0153]FIG. 1 shows an overview schematic representation of some aspectsof the invention of this application.

[0154]FIG. 2 shows a DEFT pulse sequence.

[0155]FIG. 3 shows the signal levels for cartilage and synovial fluidwith RARE and DEFT pulse sequences, both TE=14 miliseconds.

[0156]FIG. 4 shows the mean contrast to noise ratio (CNR) of cartilageto joint fluid for various MRI pulse sequences.

[0157]FIG. 5 shows the mean contrast for cartilage and joint fluid forvarious MRI pulse sequences.

[0158]FIG. 6 shows a DEFT acquisition using non-selective refocusingpulses to maximize the SNR efficiency and a partial K-Echo-Plaineracquisition gradients in order to minimize the required scan time for 3Dvolume.

[0159]FIG. 7 shows four sample images acquired with a DEFT pulsesequence combined with a partial K-Echo-Plainer acquisition in order toprovide efficient 3D coverage.

[0160]FIGS. 8A and 8B show a 3-point Dixon GRE image of the articularcartilage of medial fermorotibial compartment in a normal 35-year oldvolunteer. FIG. 13A has the subject in supine position and FIG. 13B hasthe subject in an upright position.

[0161] FIGS. 9A-9C show patient position and application of imaging coiland tracker coil for kinetic MR imaging of the knee. Patient is inupright weight-bearing position for active flexion and extension studyof the knee.

[0162]FIG. 9B is a 2D cartilage thickness map demonstrating abruptdecrease in cartilage thickness in the area of the defect (arrows). TheA thickness between the neighboring pixels can be use to define theborders of the cartilage defect. Note defused cartilage thinning in thearea enclosed by the asterisks(*).

[0163] FIGS. 10A-10C show a 3D surface registration of femoral condylesbased on T1—weighted spin-echo MR images. FIG. 6A is a baseline with aknee and neutral position. 6B is a follow-up with knee and externalrotation with a 3D view that is the identical to the one used in 6A butthe difference in knee rotation is apparent. In FIG. 6C, transformationand re-registration of Scan B into the object coordinate system of ScanA shows the anatomic match to A can be excellent.

[0164]FIG. 11A shows a 2D cartilage thickness map where a proton densityfast spin-echo MR image demonstrates a focal cartilage defect in theposterior lateral fermoral condyle (black arrows). White arrows indicateendpoints of the thickness map.

[0165]FIG. 12 shows the anatomic coordinate system in the femur and inthe tibia.

[0166]FIG. 13 shows calculation of the anatomic coordinate system frompalpable bony landmarks.

[0167]FIG. 14 shows additional marker names and locations for MR tooptical cross registration.

[0168]FIG. 15 shows the marker names and locations for the standardpoint-cluster technique protocol.

[0169]FIG. 16 shows the error in the tibial location estimate for therigid body model and the intrical deformation correction technique.

[0170]FIG. 17 shows the error in tibial orientation estimate for therigid body model and the interval deformation correction technique.

[0171] FIGS. 18A-18I show functional joint imaging.

[0172]FIG. 19 shows the superimposition of the tibiofemoral contact lineonto the 3D cartilage thickness map.

[0173]FIG. 20 shows the determination of the natural line of curvatureas the cutting plain is rotated about the transepicondyear reference,the cartilage-plain intersection results in a curve.

[0174]FIG. 21 shows the determination of the tibiofemoral contact linethrough the proximity detection and approach algorithm.

[0175]FIGS. 22A and 22B show a 2D MRI (3D SPGR) and 3D cartilagethickness map.

[0176] FIGS. 23A-E show the matching of 3D thickness maps generated fromMR images obtained with a knee neutral position and external rotation.

SPECIFIC DESCRIPTION

[0177] Overview

[0178]FIG. 1 is a schematic overview of some of the various aspects ofthe invention. While a complete description of the many aspects of theinvention is found in the specification and claims, the schematicoverview gives some of the broad aspects of the invention.

[0179] This invention relates to assessing the condition of a joint in amammal. One aspect is a method for such an assessment. The assessmentcan be done using internal images, or maps, of the cartilage alone or incombination with a movement pattern of the joint. If used alone, a mapobtained at an initial time is compared with a map obtained at a latertime to provide a view of the change in cartilage over time. Anotheraspect is a method is comparing the movement pattern for a joint of asubject being studied with the cartilage degeneration pattern of thesubject, then determining the relationship between the movement patternand the degeneration pattern. If, in determining the relationshipbetween the two patterns, one finds that the movement pattern has causedthe degeneration pattern or will continue to adversely affect thedegeneration pattern, therapy can be prescribed to minimize the adverseeffects, such as further degeneration or inflammation.

[0180] In overview, some of the systems and methods of this inventionare illustrated by the flow chart in the attached FIG. 1. FIG. 1 isbased on the full range of processes, preferably applied to a knee andsurrounding cartilage.

[0181] In FIG. 1, the first step 10 represents obtaining an image of thecartilage itself. This is typically achieved using MRI techniques totake an image of the entire knee and then, optionally, manipulating(e.g., “subtracting out” or “extracting”) the non-cartilage images asshown in step 12. Non-cartilage images typically come from bone andfluid. Preferably, the MRI is taken using external markers to providereference points to the MRI image (step 11).

[0182] If the cartilage is imaged with a 2D MRI acquisition technique,the resulting stack of 2D images so obtained can be combined into a 3Dimage, as indicated in step 14. A preferred alternative is to use 3D MRIacquisition techniques to acquire a 3D image directly. In either case,the same “non-cartilage image extraction techniques referred to in step12 can be used.

[0183] With a full 3D image captured, various “maps” or displays of thecartilage can be constructed to give a cartilage degeneration pattern.This is represented by step 16. One such display can, for example, be acolor-coding of a displayed image to reflect the thickness for thecartilage. This will allow easy visual identification of actual orpotential defects in the cartilage.

[0184] Together with or independently of the cartilage imaging, and asrepresented by parallel step 20, a 3D image of the knee joint is taken,again preferably using MRI. Many of the same techniques as applied insteps 10 to 14 are used to do this. However, as illustrated by sub-step22, it is useful to define and register a skin-external frame ofreference around the joint. This is achieved by placing fiduciarymarkers on the skin around the outside of the knee (step 22) prior totaking the image.

[0185] In addition to an image extraction technique (as described abovein step 12), an image is manipulated to enhance the image of theposition of the markers (step 24). The resulting manipulated image isused to give a 3D image of the joint and associated bones (step 26).

[0186] With the markers in place, and as shown by step 30, an additionalset of markers is placed on the skin along the outside of the leg, andan external image of the limb is obtained. Using at least two cameras,images are then taken of the subject in a static state. In addition,images are also taken of the subject while moving. This is showncollectively by step 32. The images obtained are then processed torelate the movement of the skin relative to the bone. In addition,certain calculations are performed, for example, the center of mass iscalculated. These manipulations are shown in Step 34. Further, as thefiduciary markers are still in place during the video image capture, acorrelation between the fiduciary and the additional set of markers canbe made. This is shown in step 36.

[0187] Once this marker-to-marker correlation is made, the static 3Dimage of the joint (with associated fiduciary markers) and the movementimages of the leg bones (also with fiduciary markers in place) can becombined. The fiduciary markers, therefore, serve as baselinereferences. The combination (step 40) of 3D cartilage image (from step14), 3D knee joint image (step 26), and the moving leg co-ordinates(step 34) will, after appropriate corrections, result in a displayable,3D motion image of the joint moving as per step 46.

[0188] The moving images, showing the contact areas of the knee jointcan be used in conjunction with the various “maps” or displays generatedat step 16 to provide a visual indication of potential or actualcartilage defects and help in determining their relation betweenmovement and degeneration patterns. This is shown in step 48.

[0189] Furthermore, as the various images are supported by actualmathematical quantification, real measurements (such as cartilagethickness) can be taken and compared with later or earlier measurementsand/or imaging. This allows the tracking of the progression of a defect,or conversely, continued tracking of healthy cartilage. This aids ahealth worker in providing therapy for the patients. The method allowsmonitoring and evaluation of remedial actions as well as possibletreatment prescriptions.

[0190] Thus, this invention discloses, for example, a method to examinethe relationship between articular cartilage morphology and thefunctional load bearing areas of a knee joint measured during movement.The method includes enhanced imaging techniques to reconstruct thevolumetric and biochemical parameters of the articular cartilage inthree dimensions; and a method for in vivo kinematic measurements of theknee. The kinematic measurement permits direct in vivo measurements ofcomplete six-degrees of freedom motion of the femur or the tibia orassociated bones during normal activities. This permits the study ofload bearing of articular cartilage during movement. In particular, thismethod can aid in locating cartilage defects relative to the changingload bearing areas of the knee joint during daily activities. While thevarious aspects of the invention are useful in mammals generally, theyare particularly useful for human patients.

[0191] Obtaining the Cartilage Degeneration Pattern

[0192] Imaging Articular Cartilage

[0193] In general, the joint of a patient is that place of union, moreor less movable, between two or more bones. A joint comprises cartilageand other elements such as the accompanying bones on either side of thejoint, fluid, and other anatomical elements. Joints are classified intothree general morphological types: fibrous, cartilaginous, and synovial.This invention is particularly useful for assessing synovial joints,particularly the knee.

[0194] In obtaining an image of the cartilage of a joint in a mammal, anumber of internal imaging techniques known in the art are useful forelectronically generating a cartilage image. These include magneticresonance imaging (MRI), computed tomography scanning (CT, also known ascomputerized axial tomography or CAT), and ultrasound imagingtechniques. Others may be apparent to one of skill in the art. MRItechniques are preferred.

[0195] MRI, with its superior soft tissue contrast, is the besttechnique available for assessing tissue and its defects, for examplearticular cartilage and cartilage lesions, to obtain a cartilagedegeneration can provide morphologic information about the area ofdamage. Specifically, changes such as fissuring, partial or fullthickness cartilage loss, and signal changes within residual cartilagecan be detected.

[0196] The reason MR imaging techniques are particularly suitable forcartilage is because they can provide accurate assessment of cartilagethickness, demonstrate internal cartilage signal changes, evaluate thesubchondral bone for signal abnormalities, and demonstrate morphologicchanges of the cartilage surface.

[0197] MRI provides several important advantages over other techniquesin this invention. One advantage is good contrast between cartilage,bone, joint fluid, ligaments, and muscle in order to facilitate thedelineation and segmentation of the data sets. Another is the coverageof the entire region of interest in a single scan within acceptableacquisition times. For a brief discussion of the basic MRI principlesand techniques, see MRI Basic Principles and Applications, SecondEdition, Mark A. Brown and Richard C. Semelka, Wiley-Liss, Inc. (1999).

[0198] MRI employs pulse sequences that allow for better contrast ofdifferent parts of the area being imaged. Different pulse sequences arebetter fitted for visualization of different anatomic areas, forexample, hyaline cartilage or joint fluid. More than one pulse sequencecan be employed at the same time. A brief discussion of different typesof pulse sequences is provided below.

[0199] High Resolution 3D MRI Pulse Sequences

[0200] Routine MRI pulse sequences available for imaging tissue, such ascartilage, include conventional T1 and T2-weighted spin-echo imaging,gradient recalled echo (GRE) imaging, magnetization transfer contrast(MTC) imaging, fast spin-echo (FSE) imaging, contrast enhanced imaging,rapid acquisition relaxation enhancement, (RARE) imaging, gradient echoacquisition in the steady state, (GRASS), and driven equilibrium Fouriertransform (DEFT) imaging. As these imaging techniques are well known toone of skill in the art, e.g. someone having an advanced degree inimaging technology, each is discussed only generally hereinafter. Whileeach technique is useful for obtaining a cartilage degeneration pattern,some are better than others.

[0201] Conventional T1 and T2-Weighted Spin-Echo Imaging

[0202] Conventional T1 and T2-weighted MRI depicts articular cartilage,and can demonstrate defects and gross morphologic changes. T1-weightedimages show excellent intra-substance anatomic detail of hyalinecartilage. However, T1-weighted imaging does not show significantcontrast between joint effusions and the cartilage surface, makingsurface irregularities difficult to detect. T2-weighted imagingdemonstrates joint effusions and thus surface cartilage abnormalities,but since some components of cartilage have relatively short T2relaxation times, these are not as well depicted as other preferredimaging.

[0203] Gradient-Recalled Echo Imaging

[0204] Gradient-recalled echo imaging has 3D capability and ability toprovide high resolution images with relatively short scan times. Fatsuppressed 3D spoiled gradient echo (FS-3D-SPGR) imaging has been shownto be more sensitive than standard MR imaging for the detection ofhyaline cartilage defects in the knee.

[0205] Magnetization Transfer Contrast Imaging

[0206] Cartilage, as well as other ordered tissues, demonstrate theeffects of magnetization transfer. Magnetization transfer imaging can beused to separate articular cartilage from adjacent joint fluid andinflamed synovium.

[0207] Fast Spin-Echo Imaging

[0208] Fast spin-echo imaging is another useful pulse sequence toevaluate articular cartilage. Incidental magnetization transfer contrastcontributes to the signal characteristics of articular cartilage on fastspin-echo images and can enhance the contrast between cartilage andjoint fluid. Sensitivity and specificity of fast spin-echo imaging havebeen reported to be 87% and 94% in a study with arthroscopiccorrelation.

[0209] Contrast Enhanced Imaging

[0210] The use of gadolinium for imaging of articular cartilage has beenapplied in several different forms. Direct magnetic resonance (MR)arthrography, wherein a dilute solution containing gadolinium isinjected directly into the joint, improves contrast between cartilageand the arthrographic fluid. Indirect MR arthrography, with a lessinvasive intravenous injection, can also been applied. Gadoliniumenhanced imaging has the potential to monitor glycosaminoglycan contentwithin the cartilage, which may have implications for longitudinalevaluations of injured cartilage.

[0211] Driven Equilibrium Fourier Transform.

[0212] Another 3D imaging method that has been developed is based on thedriven equilibrium fourier transform (DEFT) pulse sequence (U.S. Pat.No. 5,671,741), and is specifically designed for cartilage imaging. DEFTprovides an effective tradeoff between T2/T1 weighting and spin densitycontrast that delineates the structures of interest in the knee.Contrast-to-noise ratio between cartilage and joint fluid is greaterwith DEFT than with spoiled gradient echo (SPGR). DEFT is an alternativeapproach to SPGR. DEFT contrast is very well suited to imaging articularcartilage. Synovial fluid is high in signal intensity, and articularcartilage intermediate in signal intensity. Bone is dark, and lipids aresuppressed using a fat saturation pulse. Hence, cartilage is easilydistinguished from all of the adjacent tissues based on signal intensityalone, which will greatly aid segmentation and subsequent volumecalculations.

[0213] The basic DEFT pulse sequence is shown in FIG. 2. A conventionalspin echo pulse sequence was followed by an additional refocusing pulseto form another echo, and then a reversed, negated, excitation pulse toreturn any residual magnetization to the +z axis. This preserved themagnetization of longer T2 species, such as synovial fluid. Typical MRIparameters for cartilage are a T1-relaxation time of 900 Milliseconds(ms) and a T2-relaxation time of 40 ms, while synovial fluid has aT1-relaxation time of 3000 ms and a T2-relaxation time of 200 ms. Inaddition, synovial fluid has a 30% greater proton density thancartilage. The signal levels of cartilage and synovial fluid wereplotted in FIG. 3 for a RARE pulse sequence and for DEFT, and show thatDEFT maintains excellent contrast for any relaxation time (TR). Itachieves this contrast while maintaining a signal-to-noise ratio (SNR)efficiency (SNR)/(T_(acquisition))) that is equal to or better thanother methods with much lower contrast, such as T1-weighted GRASS.

[0214] DEFT was compared with a fast spin-echo (FSE), a gradient-echo(GRE), and a spoiled gradient-echo (SPGR) sequence with parameterssimilar to the ones published by Disler et al. The patella was scannedin 10 normal volunteer knees using a 1.5T whole-body system (GE Signa)with a 3 inch surface coil. All images were acquired with field of view(FOV) 10×10 cm, matrix 256×256 elements, slice thickness 4 mm usingfat-saturation. DEFT (400/15 [TR/TE in msec], 2 NEX (number ofexcitations), FSE (3500/15, echo train length [ETL] 8, 2 NEX (number ofexcitations), FSE (3500/15, ETL 4, 2 NEX), GRE (400/20, 30°, 2 NEX), andSPGR (50/15, 30° [flip angle], 2 NEX) images were obtained.Contrast-to-noise ratios (CNR) between cartilage and joint fluid werecalculated as:

CNR=|(SI _(Joint Fluid) −SI _(cartilage))/SI _(Background Noise)|  [Eq.1]

[0215] Contrast (C) between cartilage and joint fluid was calculated as:

C=|[(SI _(joint Fluid) −SI _(cartilage))/SI _(Joint Fluid)]×100|  [Eq.2]

[0216] In the equations SI is signal intensity. DEFT demonstratedgreater contrast-to-noise ratio and contrast between cartilage and jointfluid than SPGR, GRE, and FSE sequences (FIGS. 4 & 5). Cartilage hadintermediate signal intensity with DEFT, while joint fluid was high insignal intensity. The difference in CNR between DEFT and SPGR wasstatistically significant (p<0.001). Cartilage morphology, i.e.cartilage layers, were consistently best delineated with the DEFTsequence. At the resolution used in this study, FSE sequences sufferedfrom image blurring. Blurring was improved with ETL 4 when compared toETL8; nonetheless, even with ETL 4, cartilage morphology seen on FSEimages was inferior to the DEFT sequence. In light of these results,DEFT imaging is a preferred MRI technique.

[0217] Another Application of DEFT

[0218] DEFT was combined with a partial k-space echo-planar dataacquisition. This pulse sequence is illustrated in FIG. 6 above. A slabselective pulse in z defines the imaging volume, which is then resolvedwith phase-encoding gradients in the y and z axes, and an oscillatingEPI gradient in the x axis.

[0219] Example images acquired with this approach are shown in FIG. 7.This case was optimized for resolution, in order to image the patellarcartilage. The EPI readout acquired 5 echoes for each DEFT sequence.Partial k-space acquisition collected only 60% of the data along thex-axis. Correction for the missing data was performed using a homodynereconstruction. The image matrix was 192×192×32, with a resolution of0.5×0.5×2.5 mm, resulting in a 10×10×8 cm FOV. The echo time TE was 22ms, and the TR was 400 ms. Fat was suppressed with a fat presaturationpulse. The total scan time for this acquisition was 5 minutes.

[0220] Additional image studies that can be performed using thisapproach may require greater spatial coverage, but one can permitslightly less spatial resolution, and a longer scan time similar to theone used with the 3D SPGR approach. If one relaxes the resolution to0.75×0.75×1.5 mm, and doubles the z slab thickness and z phase encodes,the result will be a FOV of 15×15×16 cm, and a total scan time ofapproximately 15 minutes, which exactly fits the desired scan protocol.Similar to the 3D SPGR acquisition, one can acquire a first 3D DEFT scanin the sagittal plane with fat saturation. The 3D DEFT acquisition canthen be repeated without fat saturation using the identical parametersand slice coordinates used during the previous acquisition with fatsaturation. The resultant non-fat-saturated 3D DEFT images can be usedfor 3D rendering of the femoral and tibial bone contours.

[0221] In summary, Driven Equilibrium Fourier Transform is a pulsesequence preferred for cartilage imaging that provides highercontrast-to-noise ratios and contrast between cartilage and joint fluidthan SPGR, GRE, and FSE sequences. Cartilage morphology is betterdelineated with DEFT sequences than with SPGR, GRE, and FSE images. Thecombination of high anatomic detail and high cartilage-joint fluid CNRand contrast may render this sequence particularly useful forlongitudinal studies of cartilage in patients with osteoarthritis.

[0222] A Representative Example of MR Imaging is described below:

[0223] A MR image can be performed using a whole body magnet operatingat a field strength of 1.5 T (GE Signa, for example, equipped with theGE SR-120 high speed gradients [2.2 Gauss/cm in 184 μsec risetimes]).Prior to MR imaging, external markers filled with Gd-DTPA (Magnevist®,Berlex Inc., Wayne, N.J.) doped water (T1 relaxation time approximately1.0 sec) can be applied to the skin around the knee joint and optionallyat the same positions used for gait analysis in a biomotion laboratory(discussed below). The external markers can be included in the field ofview of all imaging studies. Patients can be placed in the scanner insupine position. After an axial scout sequence, coronal and sagittalT1-weighted images of the femur can be acquired using the body coil(spin-echo, TR=500 msec, TE=15 msec, 1 excitation (NEX), matrix 256×128elements, field of view (FOV) 48 cm, slice thickness 7 mm, interslicespacing 1 mm). The scanner table can then be moved to obtain coronal andsagittal images of the knee joint and tibia using the same sequenceparameters. These T1-weighted scans can be employed to identify axesthrough the femur and tibia which can be used later for defining thegeometry of the knee joint. The knee can then be placed in the knee coilwith the joint space located in the center of the coil. The knee can besecured in the coil with padding. Additionally, the foot and ankleregion can be secured in neutral position to the scanner table usingadhesive tape in order to minimize motion artifacts. A rapid scout scancan be acquired in the axial plane using a gradient echo sequence(GRASS, 2D Fourier Transform (2 DFT), TR=50 msec, TE=10 msec, flip angle40°, 1 excitation (NEX), matrix 256×128 elements, field of view (FOV) 24cm, slice thickness 7 mm, interslice spacing 3 mm). This scout scan canbe used to demonstrate the position of the knee joint space in the coiland to prescribe all subsequent high resolution imaging sequencescentered over the joint space. Additionally, using the graphic, imagebased sequence prescription mode provided with the scanner software, thescout scan can help to ensure that all external markers around the kneejoint are included in the field of view of the high resolution cartilagesensitive MR sequences.

[0224] There are several issues to consider in obtaining a good image.One issue is good contrast between cartilage, bone, joint fluid,ligaments, and muscle in order to facilitate the delineation andsegmentation of the data sets. Another is the coverage of both condylesof the knee in a single scan within acceptable acquisition times. Inaddition, if there are external markers, these must be visualized. Oneway to address these issues is to use a three-dimensional spoiledgradient-echo sequence in the sagittal plane with the followingparameters (SPGR, 3 DFT, fat-saturated, TR=60 msec, TE=5 msec, flipangle 40°, 1 excitation (NEX), matrix 256×160 elements, rectangular FOV16×12 cm, slice thickness 1.3 mm, 128 slices, acquisition timeapproximately 15 min). Using these parameters, one can obtain completecoverage across the knee joint and the external markers both inmediolateral and anteroposterior direction while achieving good spatialresolution and contrast-to-noise ratios between cartilage, bone andjoint fluid (FIGS. 8 and 9). The fat-saturated 3D SPGR sequences can beused for rendering the cartilage in three dimensions (see descriptionbelow). The 3D SPGR sequence can then be repeated in the sagittal planewithout fat saturation using the identical parameters and slicecoordinates used during the previous acquisition with fat saturation.The resultant non-fat-saturated 3D SPGR images demonstrate good contrastbetween low signal intensity cortical bone and high signal intensitybone marrow thereby facilitating 3D rendering of the femoral and tibialbone contours. It is to be understood that this approach isrepresentative only and should not be viewed as limiting in any way.

[0225] Volumes of Interest (VOI)

[0226] The invention allows a health practitioner to determine cartilageloss in a reproducible fashion and thus follow the progression of acartilage defect over time.

[0227] In one embodiment of the invention, one can use a 2D or a 3Dsurface detection technique to extract the surface of the joint, e.g.the femoral condyles, on both baseline and follow-up scans. For example,a T1-weighted spin-echo sequence can be used for surfaces extraction ofthe femoral condyles. The T1-weighted spin-echo sequence provides highcontrast between low signal intensity cortical bone and high signalintensity fatty marrow. For detection of the surface of the femoralcondyles, a step-by-step problem solving procedure, i.e., an algorithm,can convolve a data set with a 3D kernel to locate the maximum gradientlocation. The maximum gradient location corresponds to the zero crossingof a spatial location. When the kernel is designed properly, then therewill be only one zero crossing in the mask. Thus, that zero crossing isthe surface. This operation is preferably three-dimensional rather thantwo-dimensional. The surface of the joint, e.g. the femoral condyles, onthe baseline scan can be registered in an object coordinate system A.The surface of the joint, e.g. the femoral condyles, on the follow-upscan can be registered in an object coordinate system B. Once thesesurfaces have been defined, a transformation B to B′ can be performedthat best matches B′ with A. Such transformations can, for example, beperformed using a Levenberg Marquardt technique. Alternatively, thetransformations and matching can be applied to the cartilage only. Thesame transformation can be applied to the cartilage sensitive images onthe follow-up scan in order to match the cartilage surfaces.

[0228] Using the 3D surface registration of the joint on the baselinescan and resultant object coordinate system A, one can place volumes ofinterest over the area of a cartilage defect seen on the cartilagesensitive images. For example, in the knee joint, the size of thetargeted volumes of interest can be selected to exceed that of thecartilage defect in anteroposterior and mediolateral direction, e.g. by0.5 to 1 cm. If the defect is located high on the femoral condyle or inthe trochlear region, the targeted VOI can be chosen so that its sizeexceeds that of the cartilage defect in superoinferior and mediolateraldirection. The third dimension of the targeted VOI (parallel to thesurface normal of the cartilage) can be fixed, for example at 1 cm. VOIsize and placement can be manual or automatic on the baseline study.Once the targeted VOI has been placed on the image using visual orautomated computer control, the 3D coordinates of the targeted VOIrelative to the 3D contour of the joint and object coordinate system Acan be registered and saved. On follow-up studies, e.g. scansinadvertently obtained with slightly different patient position, the 3Dsurface of the joint is registered to match the orientation of thebaseline scan and the targeted VOI is then automatically placed on thejoint using object coordinate system B′ and the coordinates saved on thebaseline study. Cartilage volume within the targeted VOI on baseline andfollow-up studies can, for example, be determined using standardthresholding and seed growing techniques.

[0229] Reference Markers

[0230] When obtaining the MR images for use in this invention, whetherthe MRI is of cartilage or of bone, external reference markers can beplaced on the skin around the joint of the subject being imaged. Theexternal marker can be designed not only to show up in the MRI, but alsoto show up if an external image of the joint is obtained. The importanceand value of such unique reference markers will be discussed in moredetail hereinafter.

[0231] Thus, one embodiment of the invention is a skin reference markerthat can be used in the assessment of the condition of a joint of ahuman. Multiple skin reference markers can be placed upon one or morelimbs of a patient prior to internal imaging and external imaging. Eachskin reference marker comprises a material detectable by an imagingtechnique, a container for the material in which the containerpreferably has multiple surfaces, a means for affixing the container tothe skin (e.g. an adhesive placed on at least one surface of thecontainer in an amount sufficient to adhere the container to the skin ofa human), and a reflective material (preferably retro-reflective) placedon another surface of the container located away from the adhesive.Several imaging techniques can be used that are able to detect themarker. For example, magnetic resonance imaging is preferred, but,ultrasound, or X-ray are also useful. In the case of X-ray, furthermanipulations must be performed in which multiple X-ray images areassimilated by a computer into a 2 dimensional cross-sectional imagecalled a Computed Tomography (CT) Scan. The material detectable by animaging can be either in a liquid form or a solid form. The material canbe any imaging contrast agent or solution, e.g. a paramagnetic material.The material can be a lanthanide, such as one belonging to the yttriumgroup of rare earth metals. More specifically, the material can begadolinium. The shape of the container can be any shape allowing it tobe placed on the skin of a human. For example, it can be cubical,spherical, elliptical, discoid or cylindrical. The size of the containercan be any size, but optimally a size allowing it to be recorded by animaging machine. The longest dimension of the container can be up to 5.0cm, but preferably is about 0.25 to 2.0 cm. The reflective orretro-reflective material can be any material that is able to reflectlight directly back to the source of the light so that the position ofthe reference marker is captured by the opto-electrical recording means,e.g. a video camera. 3M Corporation makes several retro-reflectivematerials.

[0232] Manipulating Images

[0233] Once a magnetic resonance image is obtained, it can bemanipulated to improve the image by reducing unwanted, non-cartilageimages.

[0234] Segmentation

[0235] To prepare the data set for 3D rendering, the cartilage can besegmented image by image using a signal-intensity-based thresholdcombined with a seed growing technique. The femoral, tibial, andpatellar cartilage can be segmented separately based on thefat-saturated 3D SPGR or 3D DEFT sequence. Manual disarticulation can beperformed by outlining the cartilage contour in areas where the signalintensity of the articular cartilage is similar to that of adjacentstructures. The contours of the femoral, tibial, and patellar bone canbe segmented separately using the non-fat-saturated 3D SPGR or 3D DEFTsequence. Segmentation software can allow for manual editing ofcartilage thickness maps and cartilage defects detected using the aboveembodiments. In this fashion, the operator can correct erroneousdetection of cartilage defects in areas where the cartilage may benaturally thinner. Such software includes seed-growing algorithms andactive-contour algorithms that are run on standard PC's. A sharpinterface is present between the high signal intensity bone marrow andthe low signal intensity cortical bone thereby facilitating seedgrowing. Fat-saturated and non-fat-saturated 3D sequences can beacquired with the same field of view, slice thickness and slicepositions, thereby enabling superimposition and cross registration ofany resultant 3D renderings of the femoral, tibial, and patellarcartilage and bone. External reference markers can aid in registeringthe 3D data in the same object coordinate system.

[0236] 3D maps of cartilage thickness can be generated using severaldifferent techniques. One representative, but not limiting, approachuses a 3D surface detection technique which is based on a 2D edgedetector (Wang-Binford) that has been extended to 3D. This surfacedetection technique can generate surface points and their correspondingsurface normal. To smooth the contour, the program samples 25 percent ofthe surface points and fits a cubic spline to the sample points. Theprogram can compute the curvature along sample spline points and findtwo sample points that have the maximum curvature and are separated byabout half the number of voxels on the contour. These points partitionthe spline into two subcontours. For each subcontour, the program cancompute the average distance between the points and the center of themass. The program can designate the subcontour with the smaller averagedistance as the inner cartilage surface and the other subcontour as theouter cartilage surface (OCS). The intersect between the inner cartilagesurface (ICS) (located at the subchondral bone interface) and the outercartilage surface with the surface normal can be used to compute the 3Dthickness of the articular cartilage on a pixel-by-pixel basis.

[0237] Creating A Three Dimensional (3D) Image of the Cartilage

[0238] Three Dimensional Geometric Model Generation

[0239] After the 3D image of cartilage and the 3D image of joint withbones (as discussed hereinafter), are obtained, for example, the set ofsegmented two dimensional MR images can be transformed to a voxelrepresentation using a computer program developed in the AVS Express(Advanced Visual Systems, Inc., Waltham, Mass.). Every voxel has a valueof zero if it is not within an object of interest or a value rangingfrom one to 4095, depending on the signal intensity as recorded by theMRI machine. An isosurface can then be calculated that corresponds tothe boundary elements of the volume of interest. A tesselation of thisisosurface can be calculated, along with the outward pointing normal ofeach polygon of the tesselation. These polygons are written to a file ina standard graphics format (Virtual Reality Modeling Language Version1.0: VRML output language).

[0240] Visualization Software

[0241] One possible choice for the software program used to assess thecartilage degeneration pattern, the bones of the joint, and the motionpattern of the patient is a user controllable 3D visual analysis tool.The program can read in a scene, which scene consists of the various 3Dgeometric representations or “actors” (for example, VRML files of thetibia, tibia cartilage, femur, femoral cartilage), the staticrelationship transformations between these actors, and, if available,sequence of transformations describing how these actors move withrespect to each other as the patient performs some activity, such aswalking, jogging, etc.

[0242] The program can allow the user, through the use of the mouseand/or keyboard, the ability to observe the scene from arbitrary angles;to start and stop the animation derived from the motion profiles and toobserve the contact line and any cartilage lesions while the animationis running. Additionally, the user can derive quantitative informationon the scene through selecting points with the mouse.

[0243] The software program can be written in the CTT computer languageand can be compiled to run on both Silicon Graphics Workstations andWindows/Intel personal computers.

[0244] Cartilage Thickness Maps

[0245] Cartilage thickness can be determined by several methods. Oneexample is detecting the locations of the bone—cartilage and thecartilage—joint fluid interface along the surface normal using the sameedge detector described below, and subtracting them. This procedure canbe repeated for each pixel located along the bone—cartilage interface.The x, y, and z position of each pixel located along the bone—cartilageinterface can be registered on a 3D map or multiple 2D maps andthickness values are translated into color values. In this fashion, theanatomic location of each pixel at the bone cartilage interface can bedisplayed simultaneously with the thickness of the cartilage in thislocation.

[0246] The edge detector can produce accurate surface points and theircorresponding surface normal. The detector can be applied to thebaseline and the follow-up data set. For the baseline data set, both thesurface points and surface normals can be used to form locallysupporting planes (for each voxel). These planes can form anapproximated surface for the baseline skeletal site. As for thefollow-up data set, the surface points can be matched in theregistration procedure onto the surface of the baseline data set. Onecan use a newly developed 3D surface detection technique to extract thesurface of the skeletal site on both the baseline scan and the follow-upscan. Once these surfaces are detected, one can use the LevenbergMarquardt procedure to find the transformation that best matches thesetwo surfaces.

[0247] A possible approach for calculating the cartilage thickness isbased on a 3D Euclidian distance transformation (EDT). Afterthresholding, the voxels on the edge of the cartilage structure can beextracted using a slice by slice 8-neighbor search, resulting in abinary volume with the voxels on the cartilage surface having a value of1 and all others being 0. To classify these surface points as part ofthe ICS or OCS, a semi-automatic approach, which requires the user toenter a point that lies outside the cartilage structure and faces theICS, can be useful. From this point, rays are cast in all directions ofthe volume using a modified Bresenham's line drawing algorithm. If a rayhits a voxel with a value of 1, this point is classified as part of theICS: After a complete sweep of the volume, for initialization of the EDTthe ICS voxels are given a value of 0, whereas all other voxels are setto 1.

[0248] For computation of the EDT, the following representativealgorithm can be useful. It can decompose the calculation into a seriesof 3 one-dimensional transformations and can use the square of theactual distances, which accelerates the process by avoiding thedetermination of square roots.

[0249] First, for a binary input picture F={f_(ijk)} (1≦i≦L, 1≦j≦M,1≦k≦N) a new picture G={g_(ijk)} can be derived using equations (3-5)(α, β, and γ denote the voxel dimensions). Here F is a set of all voxelsinitially and G is a set of all voxels at the later time.

g _(ijk)=_(x)min{(α(i−x))² ; f _(xjk)=0;1≦x≦L}  [Eq. 3]

[0250] Thus, each point can be assigned the square of the distance tothe closest feature point in the same row in i-direction. Second, G canbe converted into H={h_(ijk)} using equation (4).

h _(ijk=) _(y)min{g _(iyk)+(β(j−y))²;1≦y≦M}  [Eq. 4]

[0251] The algorithm can search each column in the j-direction.According to the Pythagorean theorem, the sum of the square distancebetween a point (i,j,k) and a point (i,y,k) in the same column,(β(j−y))², and the square distance between (i,y,k) and a particularfeature point, g_(iyk), equals the square distance between the point(i,j,k) and that feature point. The minimum of these sums is the squaredistance between (i,j,k) and the closest feature point in thetwo-dimensional i-j-plane. The third dimension can be added by equation(5), which is the same transformation as described in the equation forthe k-direction (4).

s _(ijk)=_(z)min{h _(ijz)+(γ(k−z))²;1≦z≦N}  [Eq. 5]

[0252] After completion of the EDT, the thickness of the cartilage for agiven point (a,b,c) on the OCS equals the square root of s_(abc). The x,y, and z position of each pixel located along the bone-cartilageinterface can be registered on a 3D map and thickness values aretranslated into color values. In this fashion, the anatomic location ofeach pixel at the bone cartilage interface can be displayed simultaneouswith the thickness of the cartilage in this location.

[0253] Displaying the Degeneration Pattern

[0254] In an approach the cartilage thickness maps obtained using thealgorithm described above display only a visual assessment of cartilagethickness along the articular surface. In another approach, in order toderive a true quantitative assessment of the location, size, and depthof a focal cartilage defect, one can use an iterative approach comparingcartilage thickness of neighboring pixels located along the bonecartilage interface.

[0255] For example, assuming an image resolution of 0.5×0.5×1.0 mm andan average thickness of the articular cartilage in the femoral condylesranging between 2 to 3 mm, a 25% decrement in cartilage thickness willbe the smallest change that can be observed with most current imagingsequences. Therefore, for example, pixels along the bone—cartilageinterface that demonstrate a decrease exceeding the smallest changeobservable on a given MRI pulse sequence, in this example 25% orgreater, in overlying cartilage thickness when compared to cartilagethickness at the neighboring bone—cartilage interface pixels, can beused to define the margins of a focal cartilage defect. Other criteriacan be employed to define a cartilage defect based on comparisons ofneighboring pixels. For example, a fixed value can be used. If thedifference in cartilage thickness between neighboring pixels exceeds thefixed value, e.g. 1 mm, the pixel where this difference is observed canbe used to define the margin of the cartilage defect. This comparisoncan be performed for each pixel located along the bone—cartilageinterface for the entire data set. This comparison is preferablyperformed in three dimensions. Pixels that demonstrate a decrease incartilage thickness exceeding defined criteria but that are completelysurrounded by other pixels fulfilling the same criteria may not beconsidered to be part of the margin of the cartilage defect, but willtypically be considered to lie inside the cartilage defect.

[0256] The invention provides for means for calculating the area coveredby the cartilage defect A_(cartilage defect) and the mean thickness ofthe cartilage in the region of the defect D_(cartilage defect) as wellas the mean thickness of a defined area of surrounding normal cartilage.The thickness of the cartilage previously lost in the defect can beestimated then as:

D _(cartilage loss) =D _(normal cartilage) −D_(cartilage defect)  [Eq.6]

[0257] Since the area A of the cartilage defect is known, the volume ofcartilage loss can be computed as:

V _(cartilage loss) =A _(cartilage defect) ×D _(cartilage loss)  [Eq. 7]

[0258] Turning now to FIGS. 22A and 22B, one can see a 2D MRI (3D SPGR)and 3D cartilage thickness map. In A, the 2D MRI demonstrates a fullthickness cartilage defect in the posterior lateral femorl condyle(arrows). FIG. 22B shows a 3D cartilage thickness map generated using a3D Euclidian distance transformation. The thickness of the articularcartilage is color encoded and displayed on a pixel-by-pixel basis alongthe 3D surface of the articular cartilage. The cartilage defect is blackreflecting a thickness of zero (arrows) (M: medial, L: lateral, S:superior, I: inferior).

[0259] In FIGS. 23A-23E, one can see the matching of 3D thickness mapsgenerated from MR images obtained with the knee in neutral position andin external rotation. A. Sagittal baseline MR image (3D SPGR) with theknee in neutral position. B. Sagittal follow-up MR image of the samevolunteer obtained two weeks later with the knee in 40 degree externalrotation (note the artificially widened appearance of the femurresulting from the rotation). C. 3D thickness map generated based onbaseline MRI in neutral position. D. 3D thickness map generated based onfollow-up MRI in external rotation (note segmentation error betweencondyles in trochlear region). E. Transformation of D into the objectcoordinate system of C. Despite extreme differences in joint orientationbetween baseline and follow-up MRI scans and despite segmentationerrors, the thickness distribution on the matched follow-up scandemonstrates great similarity with that seen on the baseline scan inneutral position (in C.).

[0260] Having now described how to obtain an image of a cartilage of ajoint, both with and without external reference markers; how to enhancethe image by manipulating non-cartilage images, and creating anddisplaying 3-D images of the cartilage, ie. a 3-D map, certain aspectsof the invention are apparent.

[0261] One aspect is a method of estimating the loss of cartilage in ajoint. The method comprises

[0262] (a) obtaining a three-dimensional map of the cartilage at aninitial time and calculating the thickness or regional volume of aregion thought to contain degenerated cartilage so mapped at the initialtime,

[0263] (b) obtaining a three-dimensional map of the cartilage at a latertime, and calculating the thickness or regional volume of the regionthought to contain degenerated cartilage so mapped at the later time,and

[0264] (c) determining the loss in thickness or regional volume of thecartilage between the later and initial times.

[0265] Preferably, this aspect of the invention is directed to a volumeof interest in the cartilage, i.e., a region of the cartilage thatincludes a cartilage defect. Such a defect may be the result of adisease of the cartilage (e.g., osteoarthritis) or the result ofdegeneration due to overuse or age.

[0266] This invention allows a health practitioner to evaluate and treatsuch defects. The volume of interest may include only the region ofcartilage that has the defect, but preferably will also includecontiguous parts of the cartilage surrounding the cartilage defect.

[0267] Another aspect of the invention is a method for assessing thecondition of cartilage in a joint of a human, which method comprises

[0268] (a) electronically transferring an electronically-generated imageof a cartilage of the joint from a transferring device to a receivingdevice located distant from the transferring device;

[0269] (b) receiving the transferred image at the distant location;

[0270] (c) converting the transferred image to a degeneration pattern ofthe cartilage; and

[0271] (d) transmitting the degeneration pattern to a site for analysis.

[0272] Another aspect of the invention is a method for determining thevolume of cartilage loss in a region of a cartilage defect of acartilage in joint of a mammal. The method comprises (a) determining thethickness, DN, of the normal cartilage near the cartilage defect; (b)obtaining the thickness of the cartilage defect, DD, of the region; (c)subtracting DD from DN to give the thickness of the cartilage loss, DL;and (d) multiplying the DL value times the area of the cartilage defect,AD, to give the volume of cartilage loss. The method is useful forsituations wherein the region of cartilage defect is limited to thedefective cartilage and preferably wherein the region of the cartilagedefect includes a portion of the cartilage contiguous to the defect.

[0273] Alternatively, for step (a) the normal thickness of the defectarea could be estimated. It may be estimated from measurements ofcartilage of other subjects having similar characteristics such asgender, age, body type, height, weight, and other factors. It may beestimated from measurements of a similar ‘normal” cartilage from anothercorresponding joint (e.g., if the right knee has the defect, measure thenormal left knee). It may have been measured at an initial time T₁ whenthe cartilage was normal to provide a baseline. Other means ofdetermining the normal thickness may be available to one of skill in theart. Once the thickness D_(N) is obtained and the thickness D_(D) isobtained the two are subtracted to give the D_(L). The D_(L) ismultiplied by the area of the defect A_(D) to give the volume ofcartilage loss. By determining the volume of cartilage loss at aninitial T₁ and again at a later time T₂, one can determine the change involume loss over time.

[0274] Still another aspect of the invention is a method of estimatingthe change of a region of cartilage in a joint of a mammal over time.The method comprises (a) estimating the thickness or width or area orvolume of a region of cartilage at an initial time T₁, (b) estimatingthe thickness or width or area or volume of the region of cartilage at alater time T₂, and (c) determining the change in the thickness or widthor area or volume of the region of cartilage between the initial and thelater times. The method is particularly useful for regions ofdegenerated cartilage or diseased cartilage.

[0275] Still another aspect of the invention is a method of estimatingthe loss of cartilage in a joint. The method comprises (a) defining a 3Dobject coordinate system of the joint at an initial time, T₁; (b)identifying a region of a cartilage defect within the 3D objectcoordinate system; (c) defining a volume of interest around the regionof the cartilage defect whereby the volume of interest is larger thanthe region of cartilage defect, but does not encompass the entirearticular cartilage; (d) defining the 3D object coordinate system of thejoint at a second timepoint, T₂; (e) placing the identically-sizedvolume of interest into the 3D object coordinate system at timepoint T₂using the object coordinates of the volume of interest at timepoint T₁;(f) and measuring any differences in cartilage volume within the volumeof interest between timepoints T₁ and T₂.

[0276] Therapeutic Planning, Devising New Therapies

[0277] In another embodiment of the invention, thickness of thearticular cartilage can be estimated using an imaging test. This imagingtest can be an x-ray, ultrasound, CT scan or MRI scan. Thickness can bedetermined using a 3D Euclidian distance transformation as well as othertechniques feasible for this purpose. Thickness can be determined inselected regions or across the entire cartilage of a joint. Thicknesscan be determined in areas adjacent to diseased cartilage, in areas ofdiseased cartilage and in areas of normal cartilage.

[0278] Furthermore, the curvature of the cartilage can be determined.For this purpose, the curvature of the inner cartilage surface, i.e. thesurface facing the subchondral bone, or the outer cartilage surface,i.e. the surface facing the joint cavity, can be determined. Preferably,the inner cartilage surface at the interface with the subchondral bonewill be used, since the outer cartilage surface may be subject tofraying, fissuring or more advanced stages of cartilage loss.Alternatively, the curvature of the subchondral bone can be determined.In this case, the curvature of the subchondral bone can serve as anapproximation of the curvature of the articular cartilage. Curvature canbe determined from an imaging test, typically an ultrasound, a CT or anMRI scan. Curvature can be determined from a three-dimensional model ofthe cartilage. The three-dimensional model of the cartilage can bedetermined using the 3D Euclidian distance transformation mentionedabove. Curvature can be determined in selected regions or across theentire cartilage of a joint. Curvature can be determined in areasadjacent to diseased cartilage, in areas of diseased cartilage and inareas of normal cartilage.

[0279] Using information on thickness and curvature of the cartilage, aphysical model of the surfaces of the articular cartilage and of theunderlying bone can be created. This physical model can berepresentative of a limited area within the joint or it can encompassthe entire joint. For example, in the knee joint, the physical model canencompass only the medial or lateral femoral condyle, both femoralcondyles and the notch region, the medial tibial plateau, the lateraltibial plateau, the entire tibial plateau, the medial patella, thelateral patella, the entire patella or the entire joint.

[0280] In another embodiment, the location of a diseased area ofcartilage can be determined, for example using a 3D coordinate system ora 3D Euclidian distance transformation in combination with some of thetechniques outlined above. In another embodiment of the invention, theanteroposterior, mediolateral or superoinferior dimension of an area ormultiple areas of diseased cartilage can be determined. Furthermore, thearea, depth and volume of a cartilage defect can be determined, forexample using a 3D Euclidian distance transformation in combination withsome of the techniques outlined above

[0281] In one embodiment of the invention, information on thickness ofthe cartilage, information on curvature of the cartilage, information oncurvature of the subchondral bone or information on the location,dimensions, area, depth and volume of a defect or combinations thereofcan be used to devise a treatment. For example, the dimensions of adefect determined in this manner can be used to determine the dimensionsof a cartilage transplant, a cartilage graft, a cartilage implant, acartilage replacement material, a cartilage scaffold or a cartilageregenerating material or any cartilage repair system. Additionally, thecurvature of the inner cartilage surface or the subchondral bone can bemeasured and this information can be used to determine the shape of acartilage transplant, a cartilage graft, a cartilage implant, acartilage replacement material, a cartilage scaffold or a cartilageregenerating material or any cartilage repair system. Additionally, thethickness of normal cartilage adjacent to the defect can be measured andthe thickness values measured in this fashion can be used to determinethe optimal thickness for a cartilage transplant, a cartilage graft, acartilage implant, a cartilage replacement material, a cartilagescaffold or a cartilage regenerating material or any cartilage repairsystem. Alternatively, the thickness of the cartilage can be measured inthe contralateral joint, e.g. the knee joint, in an area correspondingto the area of diseased cartilage in the affected joint. Using any ofthese techniques or, preferably, a combination thereof, an optimal fitcan be achieved between the surrounding normal cartilage and a cartilagetransplant, a cartilage graft, a cartilage implant, a cartilagereplacement material, a cartilage scaffold or a cartilage regeneratingmaterial or any cartilage repair system thereby minimizing incongruityat the joint surface and improving the therapeutic result.

[0282] The invention provides for means to create a cast or a mold forshaping a cartilage transplant, a cartilage graft, a cartilage implant,a cartilage replacement material, a cartilage scaffold or a cartilageregenerating material or any cartilage repair system. This can begenerated using computer devices and automation, e.g. computer assisteddesign (CAD) and, for example, computer assisted modeling (CAM).

[0283] In another embodiment, the invention provides for means tomeasure and visualize the curvature of the surfaces of cartilage andbone. In another embodiment, the invention provides for means to comparethe thickness and the curvatures of surfaces of a cartilage transplantand a transplantation site, a cartilage graft and a graft site, acartilage implant and an implantation site, a cartilage replacementmaterial and an implantation site, a cartilage scaffold and a cartilagedefect, a cartilage regenerating material and an area of diseasedcartilage, or a cartilage repair system and an area of diseasedcartilage.

[0284] The invention is useful for determining the shape, dimensions andthickness of a cartilage transplant, a cartilage graft, a cartilageimplant, a cartilage replacement material, a cartilage scaffold or acartilage regenerating material or any cartilage repair system prior totreatment. For example, the shape, dimensions and thickness of acartilage transplant, a cartilage graft, a cartilage implant, acartilage replacement material, a cartilage scaffold or a cartilageregenerating material or any cartilage repair system can be designed tofollow the physical shape and thickness of the cartilage adjacent to anarea of diseased cartilage determined using the imaging test.

[0285] The invention is applicable to a host of current and futuretreatments of arthritis including but not limited to cartilagetransplants, cartilage implants, cartilage grafts, cartilage replacementmaterials, cartilage scaffolds, cartilage regenerating materials, auto-,allo- and xeno-transplants, osteochondral allo- and autografting, stemcell based repair systems and transfer systems, and, principally, anyother current and future treatments and devices for cartilage repair orregeneration.

[0286] The example described below shows one possibility how aspects ofthe invention can be implemented. It demonstrates one possible way howthe invention can be practiced. It is in no way meant to limit theinvention. One skilled in the art will easily recognize other means ofpracticing the invention.

[0287] In a first step, the cartilage and bone contours can be segmentedfrom the MR images using for example a modified live wire technique[Steines D, Cheng C, Wong A, Berger F, Napel S, Lang P.CARS—Computer-Assisted Radiology and Surgery, p. 578-583, San Francisco,2000].

[0288] From the segmented data, a 3-dimensional surface representationcan be created, which yields a triangular tesselation of the surface.For this calculation, a 3-dimensional Wang-Binford edge detector [YanCH: Measuring changes in local volumetric bone density: new approachesto quantitative computed tomography, Ph.D. thesis, 1998, Dept. ofElectrical Engineering, Stanford University] or the marching cubesalgorithm [Lorensen W E, Cline H E. Comput Graph 1987; 21: 163-169] canbe used. This surface representation can be imported into a CAD system,which is used to generate a physical model or a cast by means of rapidprototyping techniques.

[0289] The segmented data can also be used for measuring the surfacecurvature at the surface points. The curvature is calculated accordingto formula xx. If α:(a,b)→IR² is a curve defined over the parameterinterval (a,b) by α(t)=(x(t),y(t)), then the curvature K is given by[Gray A: Modern Differential Geometry of Curves and Surfaces. 1993; CRCPress]: $\begin{matrix}{{\kappa (t)} = \frac{{{x^{\prime}(t)}{y^{''}(t)}} - {{x^{''}(t)}{y^{\prime}(t)}}}{\left( {{x^{\prime 2}(t)} + {y^{\prime 2}(t)}} \right)^{\frac{3}{2}}}} & \left\lbrack {{Eq}.\quad 8} \right\rbrack\end{matrix}$

[0290] For the digitized contours that result from the segmentation,smooth derivatives for equation (8) can be obtained by convolution withdifferentiated Gaussian kernels [Worring M, Smeulders A W M. CVGIP:Image Understanding, 1993. 58(3): p. 366-382].

[0291] The curvature values can be calculated for each pixel on thesegmented surface in each slice. They can subsequently be color-mappedonto a 3-dimensional rendering of the surface for visualizationpurposes.

[0292] For full curvature information this procedure can be repeated fora direction perpendicular to the imaging plane. Of the remaining twomain directions the one with the lower degree of parallelism to thesurface can be chosen, and reformatted slices of the MR data set can beobtained for this direction. For instance, when curvature values for thefemoral condyles are calculated from sagittal MR images of the knee, thedata set would typically be reformatted for a coronal slicing plane.

[0293] The procedure of segmentation and curvature calculation can thenbe repeated for the reformatted images. A second curvature map can becalculated, yielding complementary information to the first one.

[0294] In order to compare curvature values for different surfacepatches, these can be manually or automatically registered, overlayingone surface patch on top of the other. Corresponding curvature valuesthat are calculated in the same directions can now be subtracted fromeach other, thereby yielding a measure of how well curvatures of twosurfaces match.

[0295] An example how aspects of the invention can be practicedclinically in a patient is given below. It demonstrates one possible wayhow the invention can be practiced. It is in no way meant to limit theinvention. One skilled in the art will easily recognize other means ofpracticing the invention.

[0296] A patient with arthritis of the knee joint is referred for an MRIscan. The MRI scan is performed using a cartilage sensitive MRI pulsesequence such as a fat saturated spoiled gradient echo sequence or awater selective 3D gradient echo sequence using a spectral spatialpulse. The MR images are transferred via a local network or, forexample, the internet into a computer workstation. The computerworkstation uses software to extract or segment the articular cartilagefrom the surrounding tissue. Such software can include snake algorithms,livewire algorithms, signal intensity based thresholding, or seedgrowing algorithms as well as any other technique useful for thispurpose. The software can then generate a three-dimensional map ofcartilage thickness across the femoral condyles, the tibial plateau, orthe patella. This can be achieved using a 3D Euclidian distancetransformation. Additionally, the software can provide information oncartilage curvature or curvature of the subchondral bone as describedabove. Furthermore, the software can determine the location, dimensions,size, area, depth, or volume of areas of diseased cartilage.

[0297] The information generated in this fashion can be used to generatea physical model of the area of diseased cartilage. For example, a bonereplacement material can be formed with a CAD/CAM system using the aboveinformation. One of the surfaces of the bone replacement material can beshaped so that it matches the 3D curvature of the subchondral bonesubjacent to the area of diseased cartilage. Additionally, theanteroposterior, mediolateral or superoinferior dimensions of thissurface of the bone replacement material can be such that it matches thedimensions of the area of diseased cartilage. Cartilage cells can beaffixed to the bone replacement material and can be grown on the bonereplacement material until the thickness of the resultant cartilagematches that of the thickness of the cartilage adjacent to the area ofdiseased cartilage measured on the 3D cartilage thickness map.Alternatively, a layer of cartilage of known thickness can be applied tothe bone replacement material whereby the thickness can be chosen tomatch the thickness of the cartilage adjacent to the area of diseasedcartilage measured on the 3D cartilage thickness map.

[0298] Alternatively, an artificial non-human material with propertiessimilar to cartilage can be applied to the bone replacement materialwhereby the thickness of this material can be chosen to match thethickness of the cartilage adjacent to the area of diseased cartilagemeasured on the 3D cartilage thickness map.

[0299] Alternatively, cartilage can be grown on a mold matching thecurvature of the subchondral bone in an area of diseased cartilagewhereby the dimensions of the surface of. the mold on which thecartilage is grown matches the dimensions of an area of diseasedcartilage. Cartilage can then be grown on the mold until its thicknessmatches the thickness of cartilage adjacent to the area of diseasedcartilage as measured, for example, on a 3D cartilage thickness map. Atthis point, for example, an orthopedic surgeon can excise the area ofdiseased cartilage and can implant the cartilage or cartilagereplacement material. Since the curvature of the cartilage or cartilagereplacement material matches that of the underlying subchondral bone andsince the thickness of the cartilage or cartilage replacement materialmatches that of the cartilage adjacent to the area of excised diseasedcartilage, normal or near normal joint congruity can be achieved with aresultant decrease in wear on the implanted cartilage or cartilagereplacement material and also a decrease in wear on the adjacentcartilage or the cartilage of the opposing joint surface.

[0300] Display of Biochemical Information

[0301] In addition to providing a 2D or 3D representation of themorphological properties of cartilage, the invention provides fortechniques to represent one or more biochemical components of articularcartilage.

[0302] A biochemical component includes, but is not limited to,glycosaminoglycan, water, sodium, or hyaluronic acid. Biochemical datacan be generated with other magnetic resonance based techniquesincluding the use of signal intensity measurements, relaxation timemeasurements, paramagnetic and other contrast media and sodium ratherthan proton MR imaging. Other imaging tests such as positron emissiontomography scanning can also be used for this purpose. Thus, one aspectof this invention is a method for providing a biochemically-based map ofjoint cartilage. The method comprises

[0303] (a) measuring a detectable biochemical component throughout thecartilage,

[0304] (b) determining the relative amounts of the biochemical componentthroughout the cartilage;

[0305] (c) mapping the amounts of the biochemical component through thecartilage; and

[0306] (d) determining the areas of cartilage deficit by identifying theareas having an altered amount of the biochemical component present.

[0307] Once a map is obtained, it can be used in assessing the conditionof a cartilage at an initial time and over a time period. Thus, thebiochemical map may be used in the method aspects of the invention in amanner similar to the cartilage thickness map.

[0308] For example, one aspect is a method of estimating the loss ofcartilage in a joint. The method comprises

[0309] (a) obtaining a relaxation time or biochemical map of thecartilage at an initial time and analyzing the relaxation time orbiochemical content of a region thought to contain degenerated cartilageso mapped at the initial time,

[0310] (b) obtaining a relaxation time or biochemical map of thecartilage at a later time, and time analyzing the relaxation time orbiochemical content of the region thought to contain degeneratedcartilage so mapped at the later time, and

[0311] (c) determining the change in relaxation time or biochemicalcontent of the cartilage between the later and initial times.

[0312] Preferably, this aspect of the invention is directed to a volumeof interest in the cartilage, i.e., a region of the cartilage thatincludes a cartilage defect. Such a defect may be the result of adisease of the cartilage (e.g., osteoarthritis) or the result ofdegeneration due to overuse or age. This invention allows a healthpractitioner to evaluate and treat such defects. The volume of interestmay include only the region of cartilage that has the defect, butpreferably will also include contiguous parts of the cartilagesurrounding the cartilage defect.

[0313] As discussed herein before, another aspect of the invention is amethod for assessing the condition of cartilage in a joint using arelaxation time or the biochemical map. The method comprises

[0314] (a) electronically transferring an electronically-generatedrelaxation time or biochemically based image of a cartilage of the jointfrom a transferring device to a receiving device located distant fromthe transferring device;

[0315] (b) receiving the transferred image at the distant location;

[0316] (c) converting the transferred image to a degeneration pattern ofthe cartilage; and

[0317] (d) transmitting the degeneration pattern to a site for analysis.

[0318] Another aspect of the invention is a method for determining thechange of biochemical content in a region of a cartilage defect of acartilage in joint of a mammal. The method comprises (a) determining thebiochemical content (BC_(N)) of the normal cartilage near the cartilagedefect; (b) obtaining the biochemical content of the cartilage defect(BC_(D)) of the region; and (c) subtracting BC_(D) from BCN to give thevalue of the cartilage change, BC_(D). The method is useful forsituations wherein the region of cartilage defect is limited to thedefective cartilage and preferably wherein the region of the cartilagedefect includes a portion of the cartilage contiguous to the defect.

[0319] Alternatively, for step (a) the normal content of the defect areacould be estimated. It may be estimated from measurements of cartilageof other subjects having similar characteristics such as gender, age,body type, height, weight, and other factors. It may be estimated frommeasurements of a similar ‘normal” cartilage from another correspondingjoint (e.g., if the right knee has the defect, measure the normal leftknee). It may have been measured at an initial time T₁ when thecartilage was normal to provide a baseline. Other means of determiningthe normal content may be available to one of skill.in the art. OnceBC_(N) is obtained and BC_(D) is obtained the two are subtracted to givethe Δ. By determining the change of content at an initial T₁ and againat a later time T₂, one can determine the change in biochemical contentover time.

[0320] Once a relaxation time or biochemically-based map is provided,morphological maps of articular cartilage obtained with MR imaging canbe superimposed, merged or fused with the biochemical map or data.Several different techniques can be applied in order to superimpose,merge, or fuse morphological data with biochemical data. For example, 2Dor 3D morphological data of articular cartilage can be acquired with thesame object coordinates as the biochemical data. Morphological data andbiochemical data can then be easily displayed simultaneously usingdifferent colors, opacities, and or gray scales. Alternatively, 2D or 3Dmorphological data or articular cartilage can be acquired with differentobject coordinates as the biochemical data. In this case, a 3D surfaceregistration can be applied in order to superimpose, merge, or fuse themorphological data and the biochemical data. As an alternative to 3Dobject coordinates, anatomic landmarks can be used to register themorphological data and subsequently the biochemical data in a 3D objectcoordinate system. 3D object coordinate systems can then be matched bymatching the landmarks obtained from the morphological data with thoseobtained from the biochemical data.

[0321] Thus, another aspect of this invention is a method for assessingthe condition of a subject's cartilage in a joint, the method comprisesobtaining a three dimensional biochemical representation of thecartilage, obtaining a morphological representation of the cartilage,and merging the two representations, and simultaneously displaying themerged representations on a medium. The merged representations are thenused to assess the condition of a cartilage, estimate the loss ofcartilage in a joint, determining the volume of cartilage loss in aregion of cartilage defect, or estimating the change of a region ofcartilage at a particular point in time or over a period of time. Onecan see that similar steps would be followed as spelled out for the useof a thickness map or biochemical map.

[0322] Simultaneous display of morphological data with biochemical dataprovides a useful tool to assess longitudinal changes in morphology orarticular cartilage and biochemical composition of articular cartilage,for example during treatment with chondroprotective andchondroregenerative agents.

[0323] Part of the unique aspect of this technology is that it lendsitself to assessment of a patient from a distant position after an imageis taken of the joint under evaluation. Thus one aspect of thisinvention is a method for assessing the condition of cartilage in ajoint from a distant location. The method comprises

[0324] (a) electronically transferring an electronically-generated imageof a cartilage of the joint from a transferring device to a receivingdevice located distant from the transferring device;

[0325] (b) receiving the transferred image at the distant location;

[0326] (c) converting the transferred image to a degeneration pattern ofthe cartilage; and

[0327] (d) transmitting the degeneration pattern to a site for analysis.

[0328] The degeneration pattern includes a measure of cartilagethickness or regional cartilage volume.

[0329] The electronically generated image of the cartilage preferably isan MR image and the degeneration pattern can be displayed as athree-dimensional image as a thickness pattern, a biochemical contentpattern or a merged thickness biochemical pattern. The electronicallygenerated image is transmitted via Dicom, using the internationalstandards for transmission of such images.

[0330] Another aspect of the invention is a kit for aiding in assessingthe condition of cartilage in a joint of a mammal, which kit comprises asoftware program, which that when installed and executed on a computerreads a cartilage degeneration pattern presented in a standard graphicsformat and produces a computer readout showing a cartilage thickness mapof the degenerated cartilage.

[0331] The software can be installed in a PC, a Silicon Graphics, Inc.(SGI) computer or a Macintosh computer. Preferably, the softwarecalculates the thickness or regional volume of a region of degenerationof the cartilage which does not include the entire volume of thearticular cartilage.

[0332] The Movement Pattern

[0333] To acquire a movement pattern of a joint in accordance with thisinvention, one obtains an internal image of the bones in a joint,preferably using MRI techniques, and obtains an external image of thebones in motion. The images are correlated, preferably through the useof external marker sets, to give a pattern that shows a static or movingcondition. The correlated images are then displayed and the relationbetween the movement and degeneration patterns is determined.

[0334] Obtaining An Internal Image of Joint with Bones

[0335] To obtain an internal image of a joint with the associated bones,one preferably uses MRI techniques that provide an image of the bones oneither side of the joint. Here, it is important to use the imagingtechnique that gives the best image of the bones and how they interact.Because the internal image of the bones can be combined with the imageof the bones obtained by external measurements, it is particularlyuseful, and therefore preferred, to use external reference markers thatcan be similarly-positioned to the markers used in obtaining theexternal measurements. The external markers can be placed at anylandmarks about the joint of interest. At least three markers are usedfor each limb being imaged. Preferably the markers will be made of amaterial that not only will be detected by MRI imaging techniques, butalso will be detected by external imaging techniques. The markers willbe associated with a means to affix them to the skin and preferably havean adhesive portion for adhering to the skin and a detectable entitythat will show up on the MRI image.

[0336] The preferred MRI imaging technique useful for obtaining aninternal image is a spoiled 3D gradient echo, a water selective 3Dgradient echo or a 3D DEFT sequence. A further discussion may be foundhereinbefore or in the 2^(nd) Edition of Brown and Semelka's bookentitled “MRI Basic Principles and Applications.”

[0337] Once an MR image is obtained the image is manipulated to enhancethe image of the bones. Procedures similar to those discussedhereinbefore for cartilage may be used, but modified for application tobone images.

[0338] Creating Three-Dimensional (3D) Image of Joint/Bones

[0339] Three-Dimensional Geometric Model Generation

[0340] After the 3D image of a joint with bones, the set of segmentedtwo dimensional MR images can be transformed to a voxel representationinside AVS Express (Advanced Visual Systems, Inc., Waltham, Mass.).Every voxel has a value of zero if it is not within an object ofinterest or a value ranging from one to 4095, depending on the signalintensity as recorded by the 1.5 T MR. An isosurface can then becalculated that corresponds to the boundary elements of the region ofinterest. A tesselation of this isosurface can be calculated, along withthe outward pointing normal of each polygon of the tesselation. Thesepolygons can then be written to a file in a standard graphics format(Virtual Reality Modeling Language Version 1.0).

[0341] As discussed hereinbefore, the use of reference markers on theskin around the joint and the bones can provide an image that can laterbe matched to the reference markers for the cartilage image and the boneimages obtained from external measurements.

[0342] Alternatively, a semi-automated, 3D surface-based registrationtechnique that does not require the use of an external frame or fiducialmarkers can be used. This 3D surface-based registration technique can beused to match the anatomic orientation of a skeletal structure on abaseline and a follow-up CT or MRI scan. We extended a robust andaccurate 2D edge detector (Wang-Binford) to 3D. This detector isdescribed hereinbefore.

[0343] A registration technique for the femoral condyles and the tibialplateau is shown in FIG. 10. It shows an example where 3D surfaces ofthe femoral condyles were extracted from two differently orientedT1-weighted spin-echo MRI scans (baseline A and follow-up B,respectively) obtained in the same patient in neutral position (A) andin 40 degree external rotation (B). The 3D surfaces were used to derivea coordinate transformation relating the two scans. FIG. 10Cdemonstrates the use of the derived transformation to re-register scan Bin the object coordinate system of scan A. Such a transformationrelating two T1-weighted scans can then be used to register DEFTcartilage-sensitive scans that are acquired in the same respectiveorientations as the A and B T1-weighted scans.

[0344] We performed the registration using a Sun Sparc 20 workstationwith 128 MBytes of memory. The surface detection algorithm extractedapproximately 12,000 surface patches from each data set. The surfaceextraction and registration routines took about 1 hour in total.

[0345] Since the algorithm for 3D surface registration of the femoralcondyles also computes the surface normals for the medial and lateralfemoral condyles on a pixel-by pixel basis, it can form the basis fordeveloping maps of cartilage thickness. FIG. 11 shows an example of a 2Dmap of cartilage thickness derived from the surface normals of thelateral femoral condyle. FIG. 11A shows a proton density fast spin-echoMR image that demonstrates a focal cartilage defect in the posteriorlateral femoral condyle (black arrows). White arrows indicate endpointsof thickness map. FIG. 11B is a 2D cartilage thickness map thatdemonstrates abrupt decrease in cartilage thickness in the area of thedefect (arrows). The Δ thickness between neighboring pixels can be usedto define the borders of the cartilage defect. Note diffuse cartilagethinning in area enclosed by the astericks (*).

[0346] In another embodiment, cartilage sensitive images can be usedinstead of T1-weighted or T2-weighted scans and the surface match can beperformed based on the cartilage contour.

[0347] Alternatively, anatomic landmarks present on both baseline andfollow-up scans can be used to match the data obtained during thebaseline and those obtained during the follow-up scan. Anotheralternative for matching the baseline and the follow-up scan includesthe use of external or internal fiducial markers that can been detectedwith MR imaging. In that case, a transformation is performed thatmatches the position of the markers on the follow-up scan with theposition of the markers on the baseline scan or vice versa.

[0348] Obtaining An External Image of Joint/Bones

[0349] Before merging or superimposing morphological maps of articularcartilage obtained by MR imaging with biomechanical data, one mustobtain the biomechanical data. Such biomechanical data include, but arenot limited to, estimations of static loading alignment in standing orweight-bearing position and lying or non-weight-bearing position, aswell as during joint motion, e.g., the movement of load-bearing pathwayon the cartilage in the knee joint during gait. Biomechanical data maybe generated using theoretical computations, based on data stored in adatabase that can be accessed by calling up and screening for certaincharacteristics. Alternatively, gait analysis may be performed for anindividual and data obtained during gait analysis may be merged or fusedwith morphological MRI data. Morphological data and biomechanical datacan then be easily displayed simultaneously using different colors,opacities, and or gray scales. Additionally, the load-bearing pathway,for example around a cartilage defect, can be plotted or superimposedonto morphological maps.

[0350] Preferably, reference markers or fiducial markers can be appliedto the external surface on the skin overlying the joint. These markersadhere to the skin are typically made of materials that can be detectedwith MRI and that can be used to register joint motion duringbiomechanical analysis, e.g. gait analysis. These markers can then beused to correlate the morphological with the biomechanical data.

[0351] Simultaneous display of morphological data with biomechanicaldata provides a useful tool to assess the load pathway applied toarticular cartilage and inside and around cartilage defects. Estimationof load pathway applied in and around a cartilage defect can be used toassess a cartilage defect and to guide the choice of therapy, e.g.treatment with chondroprotective or chondroregenerative agents,osteochondral allografting, cartilage transplantation, femoral or tibialosteotomy, or joint replacement surgery.

[0352] Recording Static Joint/Bones and Joint/Bones in Movement

[0353] In obtaining an external image of the bones on either side of ajoint, one must record a static image as well as a moving image of thesubject joint and bones. For analysis of the knee joint, gait analysistechniques have been shown to be very effective in generating accurate,reproducible data on the six degree of freedom motion of the knee. Themotion of the knee joint can be quantified in terms of flexion, rotationand displacement. Fidelity in the dynamic visualizations of subjectspecific MR generated knee geometry and subsequent contact surfacedetermination call for a high degree of accuracy for the motion captureportion of the studies.

[0354] Gait Analysis Activities

[0355] In performing a gait analysis, a subject is tested standingstill, laying down, walking or running on a level surface, flexing a legin a standing position, ascending and descending stairs, flexing the legin a seated position, and the like. The level walking measurements caninclude, but is not limited to, six stride cycles for each side over arange of walking speeds. The subject can be instructed to walk at acomfortable speed (normal), slower than normal and faster than normal.Typically, this protocol produces gait measurements over a range ofwalking speeds. The standing and laying portions of the protocol can beused in the cross registration to the MR data. The instrumentationpreferably includes, at least a two camera, video-based opto-electronicsystem for 3-D motion analysis, a multi-component force plate formeasurement of foot-ground reaction force and a computer system foracquisition, processing and analysis of data.

[0356] Anatomic Coordinate Systems

[0357] Currently, the anatomic coordinate systems are defined throughbony landmarks which can be identified through palpation. To describethe motion of the underlying bones in terms of the global coordinatesystem a subset of the markers in a point cluster technique (discussedhereinafter) are referenced to bony landmarks on the femur and tibia.Techniques described previously by Hopenfeld and Benedetti can be usedto locate these bony landmarks. The anatomic coordinate systems used canbe similar to that previously described by LaFortune with the exceptionof the origin of the femoral coordinate system. For the thigh segment, acoordinate system is located in the femoral condyles. The femoralcondyles medial(M)-lateral(L) axis (FIG. 12) runs through thetrans-epicondylar line (a line drawn between the medial-lateral femoralepicondyles). The midpoint of this axis is the origin. Theinferior(I)-superior(S) axis runs parallel to the long axis of thefemur, passing through the midpoint of the trans-epicondylar line. Theanterior(A)-posterior(P) axis is the cross product of the medial-lateraland inferior-superior axes. The final position of the inferior-superioraxis is made orthogonal to the anterior-posterior and medial-lateralaxis through a cross product operation (FIG. 13). For the shank segment,the tibial coordinate system begins with the medial-lateral axis runningthrough the most medial and lateral edges of the plateau. Theinferior-superior axis is perpendicular to the medial-lateral axispassing through the tibial eminence. The anterior-posterior axis is thecross product of the medial-lateral and inferior-superior axes.

[0358] Placement of Markers Prior to Activity

[0359] In assessing a joint, the lower extremity can be idealized as 3segments with six degree-of-freedom joints at the knee and ankle. Forthe mobile activities described above, at least 3 markers per segmentare used. FIG. 14 shows 21 passive retro-reflective markers located onthe leg: some at bony prominences (greater trochanter, lateralmalleolus, lateral epicondyle, lateral tibial plateau), some clusteredon the thigh and shank (Fa1-3,11-3, Fp1-3; Ta1-3, T11-13). Additionally,two markers are placed on the foot at the lateral aspect of thecalcaneus and base of the fifth metatarsal and one on the pelvis attheiliac crest). During the static activities (standing still, layingdown) 7 additional markers are placed: medial malleolus, medialepicondyle, medial tibial plateau, medial and lateral superior patella,medial and lateral inferior patella. The eight markers nearest to theknee joint can be filled with Gadolinium, and can be be replaced atthese same locations prior to the MR images (FIG. 15). The locations canbe marked with a non-toxic marker-pen.

[0360] Reference Database

[0361] The reference database is typically a compendium of demographicand motion analysis data for all subjects whose data has been processedby a central processing site. This database can contain fieldsdescribing each of the subject's name, age, height, weight, injurytypes, orthopedic medical history, other anatomic measurements (thighlength, shank length, shoe size, etc.). The database can also containthe results of any and all gait analysis run on these patients. This caninclude, for all activities tested (walk, run, jog, etc.), a number ofpeak valves (peak knee flexing, peak hip adduction movement; toe-out,angle, etc). along with the motion trajectories of the limb segmentswhile the subjects are performing different activities.

[0362] In order to obtain a typical motion profile, the sex, age,height, weight, limb length, and type of activity desired can be enteredas an average into the database. The database searches for a set ofsubjects most closely watching the input average. From this set of data,a typical motion pattern is distilled and a data set is output. Thisdata set can include, over a time interval, the motion characteristics:hip/knee/ankle/flexion/extension angles, knee/hip/ankleadduction/abduction angles, movement, stride length, cadence, etc. Thisdata can then be used to drive an animation of the motion of the desiredjoint.

[0363] Process Image of Joint/Bones

[0364] Calculation of Limb Segment Parameters

[0365] Each limb segment (thigh, shank and foot) can idealized as arigid body with a local coordinate system defined to coincide with a setof anatomical axes (the assumption of rigidity is dropped in calculatingthe location of the femur and tibia). The intersegmental moments andforces can be calculated from the estimated position of the bones, theground reaction force measurements, and the limb segment mass/inertiaproperties. The moment at the knee can be resolved into a coordinatesystem fixed in a tibial reference system with axes definingflexion-extension, abduction-adduction, and internal-external rotation.

[0366] This approach provides results in a range of patients in a highlyreproducible manner. Typically the magnitudes of the moments aredependent on walking speed. To control for the influence of walkingspeed, the walking speed closest to 1 meter/second is used. This speedis within the normal range for the type of patients for which theinvention is particularly useful. In addition to the gait trialcollected at 1 meter/second, self-selected speeds can also be evaluatedto give a good correlation between gait-quantitative estimates of jointload lines and other measures when using self-selected speeds. In orderto test patients under their typical daily conditions, medicationsshould not be modified prior to gait analyses.

[0367] Point Cluster Technique

[0368] The Point Cluster Technique (PCT) movement analysis protocol isan extensible and accurate approach to bone motion estimation.Basically, a number of retro-reflective markers (e.g. retro-reflectivematerial from 3M, Corp.) are attached to each limb segment underobservation. Multiple video cameras can acquire data with the subjectstanding still and during activities of interest. An over-abundance ofmarkers on each limb segment is used to define a cluster coordinatesystem, which is tied to an anatomically relevant coordinate systemcalculated with the subject at rest.

[0369] The standard PCT transformations are described below. In short,each marker is assigned a unit mass and the inertia tensor, center ofmass, principal axes and principal moments of inertia are calculated. Bytreating the center of mass and principal axes as a transformation,local coordinates are calculated. Another set of coordinate systems isestablished; limb segment specific anatomic landmarks are identifiedthrough palpation and a clinically relevant coordinate system defined.For the femur and tibia, these anatomic coordinate systems are shown inFIG. 12. The transformation from the reference cluster coordinate systemto the anatomic coordinate system is determined with the subject at restby vector operations. During an activity, the transformation from theglobal coordinate system to the cluster coordinate system is calculatedat each time step. To place the anatomic coordinate in the global systemduring the activity, the reference coordinate system to anatomic systemtransformation is applied, followed by the inverse global coordinatesystem to cluster coordinate system transformation for each time step.

[0370] In the Point Cluster Technique (PCT) a cluster of N markers canbe placed on a limb segment of the subject. The location vector of eachmarker in the laboratory coordinate system is denoted as G(i,t) formarker i, (i=1,2, . . . , N) at time t, t_(o)≦t≦t_(f). A unit weightfactor is assigned to each marker for the purpose of calculating thecenter of mass, inertia tensor, principal axes and principal moments ofinertia of the cluster of markers. The cluster center of mass andprincipal axes form an orthogonal coordinate system described as thecluster system. The local coordinates of each of the markers relative tothis coordinate system are calculated. Then

G(i,t)=C(t)+E(t)•L(i,t)=T _(c)(t)•L(i,t)i=1 . . . N

[0371] where G(t) is a matrix of all marker coordinate vectors, C(t) isthe center of mass of G(t), E(t) is the matrix of eigenvectors of theinertia tensor of G(t), and L(i,t) are the local coordinates of markeri.

[0372] These markers are observed by opto-electronic means while thesubject performs activities and while standing completely still in areference position. With the subject in this same reference position, asubset of the markers is observed relative to the underlying bones byother techniques, which might include x-rays, CT scan, or palpation.

[0373] The measured marker locations are defined with respect to theunobservable location and orientation of the bone by

G(i,t)=P(t)+O(t)•R(i,t)=T _(b) (t)•R(i,t)i=1 . . . N

[0374] where P(t) is the location and O(t) is the orientation of acoordinate system embedded in the bone and R(i,t), also unobservable,are the trajectories of the markers relative to the underlying rigidbody coordinate system at time t. The bone and cluster systems are eachorthogonal systems, related by the rigid body transformation T_(bc)(t):

L(i,t)=T _(bc)(t)•R(i,t)

[0375] substituting and eliminating R(i,t) yields

T _(b)(t)=T _(c)(t)•T _(cb)(t)

[0376] To maintain physical consistency, T_(cb)(t)=T_(bc)(t)⁻¹ must bethe inertia tensor eigendecomposition transformation of R(i,t). OnceR(i,t) are specified, T_(cb)(t) and subsequently T_(b)(t) arecalculable.

[0377] Point Cluster to Anatomic Coordinate System Transformation

[0378] From these equations one can also relate the global coordinatesystem with respect to a limb segment system. As an example of how thesesystems can be used to describe joint motion, one can consider thetibio-femoral joint. The motion that is of interest is how the femoralcondyles move with respect to the tibial plateau. This is done by firstdefining a set of coordinate axes in the femoral condyles and the tibialplateau.

[0379] A coordinate system is located in both the femoral condyles andthe tibial plateau. The femoral condyles medial-lateral (ML) axis runsthrough the trans-epicondylar line (TEL), a line drawn between the MLfemoral epicondyles. The midpoint of this axis is the origin. Theinferior-superior (IS) runs parallel to the long axis of the femur,passing through the midpoint of the TEL. The anterior-posterior (AP) isthe cross product of the ML and IS axes. The tibial coordinate systembegins with the ML axis running through the most medial and lateraledges of the plateau. The IS axis is perpendicular to the ML axispassing through the tibial eminence. The AP axis is the cross product ofthe ML and IS axes. These are known as the anatomic coordinate system(A(t)_(thigh), A(t)_(shank)).

[0380] Relating the cluster system to the anatomic coordinate system isdone by use of another transformation matrix. This is done by relatingthe thigh cluster to a cluster of markers, a sub cluster, that isrelated to the femoral condyles and femur (cluster to anatomictransformation).

R(t)_(thigh) =U(t)_(high) A(t)_(thigh)

[0381] The tibia has a similar transformation matrix.

R(t)_(shank) =U(t)_(shank) A(t)_(shank)

[0382] Therefore, from a cluster of markers in the global system, motionof the femur with respect to the tibia can be determined by:

TS(t)=A(t)_(thigh) •G(t)_(thigh) •R(t)_(shank) •A(t)_(shank)

[0383] Here TS(t) is the motion of the thigh with respect to the shank.

[0384] Angles are calculated by a projection angle system, an axis fromthe femoral anatomic system and one from the tibia are projected onto aplane in the tibial coordinate system. For example, flexion/extensioncan be determined by projecting the IS axis of the femur and tibia ontothe sagittal plane (AP-IS plane) of the tibia.

[0385] Validation of the Point Cluster Technique

[0386] The point cluster technique was evaluated as a method formeasuring in vivo limb segment movement from skin placed markerclusters. An Ilizarov device is an external fixture where 5 mm diameterpins are placed directly into the bone on either side of a bony defect.The rigid external struts affixed to these pins form a rigid systemfixed in the underlying bone. Two subjects were tested with Ilizarovfixation devices. One subject had the Ilizarov device placed on thefemur and second subject had the device placed on the tibia. Eachsubject was instrumented with point clusters placed on the thigh andshank segment. In addition, markers were placed on the Ilizarov deviceto establish a system fixed in the underlying bone.

[0387] The relative angular movement and translational displacementbetween the system affixed in the bone and the point cluster coordinatesystem were calculated while ascending a 20-cm step (Step Test). Angularchanges between the three orthogonal axes fixed in the bone versus threeaxes in the point cluster were calculated. The average difference overthe trials for three axes were 0.95±1.26, 2.33±1.63, and 0.58±0.58degrees. Similarly, the average error for the distance betweencoordinate systems was 0.28±0.14 cm. The second subject with theIlizarov device placed on the femur could not perform the Step-Test, butwas able to perform a weight-bearing flexion test where his knee flexedto approximately 20° from a standing position. The average changebetween the coordinate origin was 0.28±0.14 cm. The changes in axisorientation were 1.92±0.42, 1.11±0.69 and 1.24±0.16 degrees.

[0388] The simultaneously acquired motion for a coordinate systemembedded in bone (Ilizarov system) and a set of skin-based markers wascompared. At every time instant the location and orientation of theIlizarov system, the rigid body model skin marker system, and theinterval deformation technique skin marker system were determined. Thechange in the transformation from the Ilizarov system to one of the skinmarker systems over time is a measure of the deformation unaccounted forin the skin marker system.

[0389] The interval deformation technique produced a substantialimprovement in the estimate of the location and orientation of theunderlying bone. For perfectly modeled motion there would be no relativemotion between the Ilizarov system and the skin marker system over thetime interval. The change in the transformation from the Ilizarov systemto the skin marker systems are shown in FIGS. 14 and 15, for locationand orientation respectively, for both a rigid body model and theinterval deformation technique. For this single data set, the locationerror was reduced from 7.1 cm to 2.3 cm and the orientation error from107 degrees to 24 degrees, with the error summed over the entire timeinterval. The subject performed a 10 cm step-up; the marker deformationwas modeled as a single Gaussian function.

[0390] Deformation Correction

[0391] There are a number of algorithmic alternatives available tominimize the effects of skin motion, soft tissue deformation, or muscleactivation that deform the externally applied markers relative to theunderlying bone. The Point Cluster Technique decreases the effects ofmarker movement relative to the underlying bone through averaging. Ifmore correction is required, one of a number of deformation correctiontechniques may be added. In order of increasing computational complexityand deformation correction ability, these are rigid body linear leastsquare error correction, global optimization correction, anatomicartifact correlation correction and interval deformation correction.

[0392] An overview of the Interval Deformation Correction Technique isgiven below. In short, the technique provides a maximum likelihoodestimate of the bone pose, assuming that each marker on a limb segmentdeforms relative to the underlying bone in some functional form. Thetechnique parameterizes these functional forms and then performs amulti-objective non-linear optimization with constraints to calculatethese parameters. This is an extremely computationally intensivetechnique, with the current instantiation of the algorithm requiring 6-8hours per limb segment of running time on 266 MHz Pentium 2 computer.

[0393] Interval Deformation Technique

[0394] Since Tc can be calculated directly from the global coordinatesof the markers, the remainder of this development only examines thedetermination of R(i,t) and subsequently T_(cb)(t). For this reducedproblem, the input data is the local coordinates in the cluster systemL(i,t) for all i, T_(o)≦t≦t_(f). It can be assumed that each marker hassome parameterized trajectory, d(a_(ij), t), relative to the underlyingbone at each time step, with independent and identically distributednoises v(i,j,t)

R _(j)(i,t)=d(a _(ij) , t)+v(ij,t)j=1 . . . 3i=1 . . . N

[0395] or, equivalently

R(i,t)=F(a _(i) , t)+v(i,t)i=1 . . . N

[0396] where a_(ij) is a vector of parameters for marker i, ordinate j;a_(i) is a vector of parameters combining all of the parameters for allof the ordinates of marker i. Then the estimate of the data, M(i,t), canbe given by

M(i,t)=T _(bc)(t)•R(i,t)

[0397] Without further restrictions the problem is indeterminate, as thelocations of the markers in the bone system R(i,t) are never observablewith the opto-electronic system. The indeterminate problem can beconverted to a chi-squared estimate problem through a series of steps.An observation of the truly unobservables at the time boundaries isinferred; that is, it is assumed that T_(cb) (t≦t_(o)) and T_(cb)(t≧t_(f)) are observed. The value of T_(cb) can be selected depending onthe activity being studied. For example, consider the step up activity,where the subject starts and stops in the reference position. For thisactivity the body is not deforming outside the estimation interval; thatis, the markers are not moving with respect to the bone:

T _(cb)(t<t _(o))=T _(cb)(t=t _(o))

[0398] and

T _(cb)(t>t _(f))=T _(cb)(t _(f))

[0399] It can now be assumed that the noise functions v(i, j, t) arenormal distributions with individual standard deviations σ(i, j, t), theprobability P(ij,t) of the data for ordinate j, marker i, time t being arealization of the stochastic process is given by:${P\left( {,j,t} \right)} \propto {\exp \left( {{- \frac{1}{2}}\left( \frac{{L\left( {,j,t} \right)} - {M\left( {,j,t} \right)}}{\sigma \left( {,j,t} \right)} \right)^{2}} \right)}$

[0400] Provided the noise functions v(i, j, t) are independent of eachother, the probability of the entire data set being a realization is aproduct of each of the individual probabilities:${P\left( {,j,t} \right)} \propto {\prod\limits_{i = l}^{N}\quad {\prod\limits_{j = l}^{3}\quad {\prod\limits_{t = t_{0}}^{f_{t}}\quad {\exp \left( {{- \frac{1}{2}}\left( \frac{{L\left( {,j,t} \right)} - {M\left( {,j,t} \right)}}{\sigma \left( {,j,t} \right)} \right)^{2}} \right)}}}}$

[0401] Maximizing this probability can be equivalent to minimizing thenegative of its logarithm, yielding the familiar chi-square criteria. Asan intermediate step the following error matrices can be defined:$\begin{matrix}{{{X\left( {a,t} \right)} \ni {X\left( {a,t} \right)}_{i,j}} = \left( \frac{\left( {{L\left( {,j,t} \right)} - {M\left( {,j,t} \right)}} \right)}{\sigma \left( {,j,t} \right)} \right)^{2}} & {i = {1\quad \ldots \quad N}} & {j = {1\quad \ldots \quad 3}}\end{matrix}$${X(a)} = {\sum\limits_{t = t_{0}}^{t_{f}}\quad {X\left( {a,t} \right)}}$

[0402] and seek a which in some sense minimizes X(a), a matrix whoseelements represent the error over the entire time interval for eachordinate of each marker. If the normal noise distribution assumption istrue, then this minimization results in the maximum likelihood estimateof the parameterization, and by inference maximum likelihood estimate ofthe transformation from the bone system to the cluster system. If thenormal noise assumption is not true, the chi-squared estimate is stillappropriate for parameter estimation; the results cannot be interpretedas a maximum likelihood estimate, but, for example, confidence regionson the estimate or the formal covariance matrix of the fit can bedetermined.

[0403] Obtaining the parameter set a is a computationally complexoperation. The approach taken was to define a scalar to represent thisentire error matrix,${f(a)} = {\sum\limits_{i = l}^{N}\quad {\sum\limits_{j = i}^{3}{X(a)}_{i,j}}}$

[0404] and seek a that minimizes f(a).

[0405] The limits on marker motion previously discussed can now beconverted into deformation constraints, which allow the formulation ofthe problem as a general non-linear programming problem. The constraintsarise from two sources; human limb segments do not deform outside asmall range, and the locations of the markers are chosen with specificproperties in mind. For computational purposes, the deformationconstraints are selected to be:

[0406] 1. The axes of the cluster system moves by less than 15 degreesrelative to the bone system.

[0407] 2. The center of mass of the cluster system moves by less than 3cm relative to the bone system.

[0408] 3. The markers move by less than 4 cm relative to the bonesystem.

[0409] 4. Each of the principal moments of inertia of the cluster systemchange by less than 25 percent from the reference values.

[0410] The Point Cluster Technique marker set was designed to ensurethat the cluster of points is non-coplanar and possess no axes ofrotational symmetry. These properties ensure a local coordinate systemthat is well defined and unambiguous over the entire time interval. Theconstraints are then:

[0411] 5. The ratio of the smallest principal moment of inertia of thecluster system to the largest is more than 5 percent; the magnitude ofthe smallest principal moment of inertia of the cluster system isgreater than some small positive value.

[0412] 6. The principal moments of each axis are different from eachother by at least 5 percent.

[0413] The general problem can then be formulated:

[0414] Minimize f(a)

aεR^(D)

[0415] Subject to:

gi(a)=0i=1 . . . m _(e)

gi(a)≦0i=m _(e)+1 . . . M

a₁≦a≦a_(u)

[0416] where D is the total number of parameters; m_(e), the number ofequality constraints, is 0; and m, the total number of constraints, is10.

[0417] The approach taken to verify the operation of the algorithmimplementation began with generating a set of 50 synthetic data setswith known characteristics. The program was then applied to all of thedata sets. The program results were then compared to the known,generated deformation. Error results were calculated for both theinterval deformation technique descried herein and for the standardrigid body model formulation.

[0418] The 50 trial data sets were processed through the algorithm. Theresults over all of the trial sets are summarized in Table I, where thecenter of mass and direction cosine error of the interval deformationtechnique and the rigid body model are compared. After processing by theinterval deformation algorithm the center of mass error has been reducedto 29% and the direction cosine error has been reduced to 19% of therigid body model error. In a t-test for paired samples, both of thesedecreases were significant at p<0.001.

[0419] Validation of the Interval Deformation Correction Technique

[0420] A subject fitted with an Ilizarov external fixation was observedwith the optoelectronic system. The Point Cluster Marker set was affixedto the subject's shank (6 markers), along with a set of four markersrigidly attached to the Ilizarov device, which is rigidly connected tothe tibia with bone pins. These four markers define a true bone embeddedcoordinate system. Data were acquired by GaitLink software (ComputerizedFunctional Testing Corporation) controlling four Qualisys camerasoperating at a video frequency of 120 Hz. Three dimensional coordinateswere calculated using the modified direct linear transform.

[0421] The subject was a 46 year old male (height 1.75 m, weight 84.1kg) fitted with a tibial Ilizarov external fixation device. The devicewas rigidly attached to the tibia with nine bone pins, located in threesets (top, middle, and bottom) of three (medial, anterior, and lateral).The clinical purpose of the device was tibial lengthening; the test onthe subject was performed two days prior to final removal of the device.The subject exhibited a limited range of motion and was testedperforming a 10 cm step-up onto a platform.

[0422] The simultaneously acquired motion for a coordinate systemembedded in bone (Ilizarov system) and a set of skin-based markers wascompared. At every time instant the location and orientation of theIlizarov system, the rigid body model skin marker system, and theinterval deformation technique skin marker system was determined. Thechange in the transformation from the Ilizarov system to one of the skinmarker systems over. time is a measure of the deformation unaccountedfor in the skin marker system.

[0423] The interval deformation technique produced a substantialimprovement in the estimate of the location and orientation of theunderlying bone. For perfectly modeled motion there would be no relativemotion between the Ilizarov system and the skin marker system over thetime interval. The change in the transformation from the Ilizarov systemto the skin marker systems are shown in FIGS. 14 and 15 for location andorientation respectively, for both a rigid body model and the intervaldeformation technique. For this single data set, the location error wasreduced from 7.1 cm to 2.3 cm and the orientation error from 107 degreesto 24 degrees, with the error summed over the entire time interval. Thesubject performed a 10 cm step-up; the marker deformation was modeled asa single Gaussian function.

[0424] Correlating Results from Gait Analysis and GeometricalRepresentations of the Bone

[0425] In correlating the load pattern obtained from a gait analysisusing, e.g. the PCT, with the geometrical representation of the bonefrom the segmented MRI data, one can be guided by the general process asdescribed below. The process allows for dynamic visualization (i.e.animations) of high-resolution geometrical representations derived fromMRI scans (or other imaging techniques). The motion of the subjectspecific anatomic elements is generally driven by data acquired from themotion (gait) lab. Fidelity of these animations requires calculation andapplication of a sequence of rigid body transformations, some of whichare directly calculable and some of which are the result ofoptimizations (the correction for skin marker deformation from rigiditydoes not use the rigid body assumption, but generates a correction thatis applied as a rigid body transform).

[0426] The process comprises:

[0427] a) acquiring data from MRI (or other imaging techniques), and PCTgait protocols;

[0428] b) directly calculating a set of transformations from the data;

[0429] c) calculating a set of transformations from optimizations, asneeded;

[0430] d) generating a 3D geometric representation of the anatomicelement from the MR data; and

[0431] e) applying the transformations of (b) and (c) to the 3Dgeometric representation.

[0432] Each of these steps are described in detail below.

[0433] Acquiring the Data from MRI (or other Imaging Techniques) and PCTGait Protocols

[0434] In the Point Cluster Technique (PCT) protocol, a patient can havea number of retro-reflective markers attached to each limb segment underobservation. Multiple video cameras acquire data with the subjectstanding still and during activities of interest.

[0435] In addition, in order to correspond activities in the gait labwith the MRI scans, another reference data set (subject standing still,prescribed posture) can be acquired using 8 additional markers clusteredabout the knee. These markers are filled with gadolinium-DTPA andcovered with a retro-reflective material to allow for correlationbetween the MRI image and the video data.

[0436] Directly Calculating a Set of Transformations from the Data

[0437] The transformations are described in detail in [Andriacchi T P,Alexander E J, Toney M K, Dyrby CO, Sum J. J Biomech Eng 1998; 120(12):743-749]. In short, each marker can be assigned a unit mass and theinertia tensor, center of mass, principal axes and principal moments ofinertia can be calculated. By treating the center of mass and principalaxes as a transformation, local coordinates arcan be e calculated.Another set of coordinate systems can also be required for thistechnique; limb segment specific anatomic landmarks can be identifiedthrough palpation and a clinically relevant coordinate system can bedefined. The required transformations are summarized in Table 1 below.

[0438] Calculating a Set of Transformations from Optimizations

[0439] There are three required transformations:

[0440] Optimization 1. One can calculate the linear least square errorrigid body transformation from the MRI common local coordinate system tothe VID common local coordinate system.

[0441] Optimization 2. For each limb segment, one can calculate thelinear least square rigid body transformation from the MRI limb segmentanatomic coordinate system to the video limb segment anatomic coordinatesystem (obtained from the gait analysis), using a subset of commonmarkers appropriate for each segment.

[0442] Optimization 3. One can calculate a correction for the deviationof the limb segment from rigidity during each time step of the activity,using the PCT with either the mass redistribution [Andriacchi T P,Alexander E J, Toney M K, Dyrby C O, Sum J. J Biomech Eng 1998; 120(12):743-749] or interval deformation algorithms [Alexander E J, Andriacchi TP: Correcting for deformation in skin-based marker systems. Proceedingsof the 3rd Annual Gait and Clinical Movement Analysis Meeting, SanDiego, Calif, 1998].

[0443] Generating a 3D Geometric Representation of the Anatomic Elementfrom the MR Data

[0444] The MR slices are segmented for the multiple anatomic andfiducial elements. The slices are combined to a voxel representation. Anisosurface can be calculated from the boundary voxel elements. Atessellation of the isosurface can be calculated, along with the outwardpointing normal for each surface element. This data can then be storedin a standard 3D graphic format, the Virtual Reality Modeling Language(VRML).

[0445] Appling the Transformation Sequence to the GeometricRepresentation

[0446] The transformation sequence is provided below in Table 1. Thistransformation sequence can be applied to each of the anatomic elementsover each time step of the activity, starting with sequence 6. TABLE 1SEQ FROM SYSTEM TO SYSTEM XFORM 1 MR Global MR Local ED1 2 MR LocalCommon Local OPT1 3 Common Local MR Anatomic ANA2 4 MR Anatomic VIDAnatomic OPT2 5 VID Anatomic VID Ref ANA3 6 VID Ref VID Deformed(t) ED37 VID Deformed(t) VID Bone(t) OPT3 8 VID Bone(t) VID Global(t) ED4

[0447] Correlating Marker Sets

[0448] As pointed out at numerous places in the specification, the useof external reference markers that are detectable by both MRI andoptical techniques can be an important and useful tool in the method ofthis invention. The use of the reference markers can form the basis foran aspect of this invention that is a method for correlating cartilageimage data, bone image data, and/or opto-electrical image data for theassessment of the condition of a joint of a human. This methodcomprises, obtaining the cartilage image data of the joint with a set ofskin reference markers placed externally near the joint, obtaining thebone image data of the joint with a set of skin reference markers placedexternally near the joint, obtaining the external bone image dataopto-eletrical image data of the joint with a set of skin referencemarkers placed externally near the joint. Using the skin referencemarkers, one can then correlate the cartilage image, bone image andopto-electrical image with each other, due to the fact that each skinreference marker is detectable in the cartilage, bone andopto-electrical data. The cartilage image data and the bone image datacan be obtained by magnetic resonance imaging, positron emissiontomography, single photon emission computed tomography, ultrasound,computed tomography or X-ray. Typically, MRI will be preferred. In thecase of X-ray, further manipulations must be performed in which multipleX-ray images are assimilated by a computer into a 2 dimensionalcross-sectional image called a Computed Tomography (CT) Scan. Theopto-electrical image data can be obtained by any means, for example, avideo camera or a movie camera. Multiple skin reference markers can beplaced on one or more limbs of the patient prior to imaging. The skinreference markers are described hereinbefore.

[0449] By a sequence of calculations a set of transformations that willtake the subject specific geometric representation of anatomic elementsdetermined from the MR image set to the optical reference coordinatesystem. From the optical reference coordinate system, the standard PointCluster Technique transformation sequence is applied to generate dynamicvisualizations of these anatomic elements during activities previouslyrecorded in the motion lab. Fidelity of these dynamic visualizations(and subsequent contact surface determination) requires the calculationand application of a sequence of rigid body transformations. Some ofthese are directly calculable and some are the result of optimizations(the correction for skin marker deformation from rigidity does not usethe rigid body assumption, but generates a correction that is applied asa rigid body transform).

[0450] The first required transformation can be from the MR globalcoordinate system to the MR center of mass/principal axis coordinatesystem. This can be done by calculating the center of mass of each ofthe individual markers, resulting in a set of eight three dimensionalpoints. Each of these points can be assigned a unit mass, and the centerof mass, inertia tensor, and principal axes can be calculated. The sameprocedure can be performed on these markers as determined by the opticalsystem, providing a transformation from the optical global system to acenter of mass/principal axis system.

[0451] If the relative orientation of the tibia and femur as determinedby the MR system and the optical system are identical, it is onlynecessary to apply the optical reference system to the anatomic systemtransformation of the MR local data. If this is not the case, anoptimization calculation can be performed to determine the rotation andtranslation of, for example, the femur with respect to the tibia. Onethen can calculate the linear least square rigid body transformationfrom the MR limb segment anatomic coordinate system to the video limbsegment anatomic coordinate system prior to applying the Point ClusterTransformations.

[0452] For visualization or contact surface determination, one canexamine the relative motion of one segment to the other, for example themotion of the femur relative to a fixed tibial frame. This can beaccomplished by applying the global to tibial anatomic system transformto all of the elements. An example of this type of visualization isgiven in FIG. 18. The Figure shows what can be referred to as functionaljoint imaging. FIG. 18A is a photograph demonstrating the position ofthe external markers positioned around the knee joint. The markers arefilled with dilute Gd-solution. B is Sagittal 3D SPGR image through themedial femorotibial compartment. Two of the external markers are seenanteriorly as rounded structures with high signal intensity. C is 3Dreconstruction of femoral and tibial bones (light grey), externalmarkers (dark grey), femoral cartilage (red), and tibial cartilage(blue) based on the original SPGR MR images. D-I show a functional jointimaging sequence at selected phases of leg extension from a seatedposition, D-F, anterior projection. The vectors represent the relativelocation and orientation of the femur with respect to the tibia. G-I isa lateral projection. These dynamic visualizations can be used todemonstrate tibiofemoral contact areas during various phases if gait orother physical activities.

[0453] Superimposition of Cartilage Thickness Map onto Subject SpecificAnatomic Model and Determination of Distance of Cartilage Defect fromLoad Bearing Line

[0454] Superimposing the cartilage thickness maps onto the subjectspecific geometric models can follow the same approach taken to bringthe MR generated geometries into the optical reference system. Since thethickness maps and the geometric models are initially in the samecoordinate system; one possible approach is to perform a simple surfacemapping of the thickness map onto the geometric model. Anotheralternative approach is to convert the thickness map directly into ageometric representation (FIG. 19).

[0455] Once the thickness map is embedded in the femoral geometry, onecan define a scalar metric that characterizes the location of anycartilage lesions relative to the point of contact line. One approach isa simple 3D distance along the surface from the center of the cartilagelesion to the point of closest approach of the contact line. Anothermetric that could be useful would be to multiply the area of the lesionby the adduction moment at that time instant, then divide by thedistance from lesion center to point of closest approach. This couldresult in a metric that increases with lesion area, adduction moment,and closeness of approach.

[0456] 46 Display Correlated Images

[0457] Determination of Anatomic and Natural Reference Lines

[0458] There are two alternative approaches one can consider fordetermining a reference line on the cartilage surfaces. One skilled inthe art will easily recognize other approaches that can be suitable forthis purpose. The first approach is based on anatomic planes; the secondis a natural approach building on the three dimensional cartilagethickness map.

[0459] The location of the pathway of loading relative to the femoraland tibial anatomy and geometry can be assessed by defining sagittalplanes bisecting the medial femoral condyle, the lateral femoralcondyle, the medial tibial plateau, and the lateral tibial plateau. Forthe medial femoral condyle, the operator can manually delete surfacepoints located along the trochlea. Then, a sagittal plane parallel tothe sagittal midfemoral plane can be defined through the most medialaspect of the medial femoral condyle followed by a sagittal planeparallel to the sagittal midfemoral plane through the most lateralaspect of the medial femoral condyle. The sagittal plane that is locatedhalfway between these two planes can be defined as the “midcondylarsagittal plane”. The intersection between the midcondylar sagittal planeand the external cartilage surface yields the “anatomic midcondylarcartilage line”. The location of the pathway of loading can be assessedrelative to the anatomic midcondylar cartilage line of the medialfemoral condyle. The identical procedure can be repeated for the lateralfemoral condyle.

[0460] The following method can be used for the medial tibial plateau: Aplane parallel to the sagittal tibial plateau plane can be definedthrough the most medial point of the medial tibial plateau. A parallelplane located halfway between this plane and the sagittal tibial plateauplane can yield the “midsagittal plane of the medial tibial plateau.”The intersection of the midsagittal plane of the medial tibial plateauand the external cartilage surface can yield the “anatomic midtibialplateau cartilage line” of the medial tibial plateau. The identicalprocedure can be repeated for the lateral tibial plateau.

[0461] In the second approach, one can calculate a “natural” line ofcurvature for each femoral cartilage component (FIG. 20). Intuitively,if one could roll the femoral condyles along a hard, flat surface, theline of contact with the flat surface would be the natural line ofcurvature. One can compare the actual tibiofemoral contact line to thisreference line. Since one cannot physically remove the femur and roll itaround, one can apply some geometric calculations to estimate thisreference line. One can begin with the trans-epicondylar reference linepreviously described. One can then generate a plane coincident with thisline oriented in an arbitrary initial position. The intersection of thisplane and the external surface of the cartilage will produce a curve.One can then take the point furthest from the trans-epicondylarreference line as the natural contact point for this plane location. Thenext step is to rotate the plane by some increment, for example by onedegree, and repeat the procedure. The operator can identify the rotationangles where the plane is intersecting the distinct medial—lateralcompartments of the cartilage, and two points can be chosen, one fromthe medial femoral condyle and one from the lateral femoral condyle. Ifcartilage defects are present, in which case a compartment will notintersect in a curve but in a set of points, one can fit a splinethrough the points, then take the peak point of the spline as thecontact point.

[0462] This can be repeated for the entire extent of the cartilage,resulting in a set of points that branch at the intercondylar notch. Onecan treat these points as two lines, and fit them with two splines.These can be the “natural” lines of curvature for each compartment.

[0463] Load Bearing Line Determination

[0464] The calculations in this section can begin with the relativemotion of the subject specific femoral anatomy with respect to thesubject specific tibial anatomy, and end with a line describing thepoint of closest approach between the femur and tibia during someactivity of daily living. A number of approaches to this problem havebeen described in the literature; Crosset, Dennis, Stiehl, and Johnsonhave all described techniques which might be applicable. One canimplement a proximity detection and approach algorithm (PDAA) as it wasspecifically designed to work with the Point Cluster Technique (albeitwith prosthetic knee joint components).

[0465] Physically, the tibial and femoral cartilage components deformunder load, leading in general to a contact patch between opposingsurfaces. As the geometric models are rigid, they will not deform underthis load, but will instead intersect in a non-realizable manner. ThePDAA has been designed to incrementally displace and rotate one of thesurfaces until a realizable contact is achieved. It is understood thatthis is not a true point contact line, but rather a reproduciblerepresentation of contact location (FIG. 21).

[0466] The MR generated subject specific geometries can be used todetect rigid body contact proximity when the subject is in fullextension. The femoral component can then be incrementally displaceduntil simultaneous medial and lateral condyle contact occur. This is afirst order approximation to the location of the contact point; slipvelocity calculations can then be used to determine the final estimateof the contact point. The next time step in the activity can now beexamined, using the previous time step solution as a starting point forthe calculation. The full extension time step can be chosen to matchwith the static reference posture; should it be necessary, one can addin other reference postures.

[0467] Once the contact points have been determined for all time stepsof the activity, one can map the locations of these points onto thefemoral cartilage. A coordinate system can be defined on the surface ofthe femoral cartilage, choosing as a reference line the point of contactthe femoral component would have had were it rolled along a flat plane.This allows one to determine a contact line relative to the subjectspecific anatomy.

[0468] Provide Therapy

[0469] A 2D or 3D surface registration technique can be used as an aidto providing therapy to match the anatomic orientation of the cartilagethickness map of a baseline and follow-up scan of a patient. There-registered cartilage thickness map of the follow-up scan can then besubtracted from the baseline scan. This will yield the thicknessdifference, i.e. cartilage loss, as a function of x, y, and z. This canalso be expressed as percentage difference.

[0470] The invention provides for techniques to assess biomechanicalloading conditions of articular cartilage in vivo using magneticresonance imaging and to use the assessment as an aid in providingtherapy to a patient. In one embodiment, biomechanical loadingconditions can be assessed in normal articular cartilage in variousanatomic regions. In the knee joint, these anatomic regions include theposterior, central, and anterior medial femoral condyle, the posterior,central, and anterior medial tibial plateau, the posterior, central, andanterior lateral femoral condyle, the posterior, central, and anteriorlateral tibial plateau, the medial and lateral aspect of the trochlea,and the medial and lateral facet and the median ridge of the patella.Since biomechanical loading conditions are assessed in vivo based on theanatomic features of each individual patient, a risk profile can beestablished for each individual based on the biomechanical stressesapplied to cartilage. In this fashion, patients who are at risk fordeveloping early cartilage loss and osteoarthritis can be identified.For example, patients with a valgus or varus deformity of the knee jointwill demonstrate higher biomechanical stresses applied to the articularcartilage in the medial femorotibial or lateral femorotibial orpatellofemoral compartments than patients with normal joint anatomy.Similarly, patients with disturbances of joint congruity willdemonstrate higher biomechanical stress applied to certain regions ofthe articular cartilage. Such disturbances of joint congruity are oftendifficult to detect using standard clinical and imaging assessment. Theamount of stress applied to the articular cartilage can be used todetermine the patient's individual prognosis for developing cartilageloss and osteoarthritis. In another embodiment, biomechanical loadingconditions can be assessed in normal and diseased articular cartilage.An intervention that can alter load bearing can then be simulated. Suchinterventions include but are not limited to braces, orthotic devices,methods and devices to alter neuromuscular function or activation,arthroscopic and surgical procedures. The change in load bearing inducedby the intervention can be assessed prior to actually performing theintervention in a patient. In this fashion, the most efficacioustreatment modality can be determined. For example, a tibial osteotomycan be simulated in the manner and the optimal degree of angularcorrection with regard to biomechanical loading conditions of normal anddiseased cartilage can be determined before the patient will actuallyundergo surgery.

[0471] Estimation of biomechanical forces applied to normal cartilagecan be used to determine a patient's risk for developing cartilage lossand osteoarthritis. Estimation of forces applied in and around acartilage defect can be used to determine the prognosis of a cartilagedefect and to guide the choice of therapy, e.g. treatment withchondroprotective or chondroregenerative agents, osteochondralallografting, cartilage transplantation, femoral or tibial osteotomy, orjoint replacement surgery.

[0472] Having now provided a full discussion of various aspects of thetechnology relating to this invention, several further aspects of theinvention can be seen.

[0473] One aspect of the invention is a method of assessing thecondition of a joint in a mammal. The method comprises:

[0474] (a) comparing the movement pattern of the joint with thecartilage degeneration pattern of the joint; and

[0475] (b) determining the relationship between the movement pattern andthe cartilage degeneration pattern

[0476] Another aspect of the invention is a method for monitoring thetreatment of a degenerative joint condition in a mammal. The methodcomprises

[0477] (a) comparing the movement pattern of the joint with thecartilage degeneration pattern of the joint:

[0478] (b) determining the relationship between the movement pattern andthe cartilage degeneration pattern;

[0479] (c) treating the mammal to minimize further degeneration of thejoint condition; and

[0480] (d) monitoring the treatment to the mammal.

[0481] Still another aspect of the invention is a method of assessingthe rate of degeneration of cartilage in the joint of a mammal, whereinthe joint comprises cartilage and the bones on either side of thecartilage, which method comprises

[0482] (a) obtaining a cartilage degeneration pattern of the joint thatshows an area of greater than normal degeneration,

[0483] (b) obtaining a movement pattern of the joint that shows wherethe opposing cartilage surface contact,

[0484] (c) comparing the cartilage degeneration pattern with themovement pattern of the joint, and

[0485] (d) determining if the movement pattern shows contact of onecartilage surface with a portion of the opposing cartilage surfaceshowing greater than normal degeneration in the cartilage degenerationpattern.

[0486] Another aspect of the specification is a method for assessing thecondition of the knee joint of a human patient, wherein the knee jointcomprises cartilage and associated bones on either side of the joint.The method comprises

[0487] (a) obtaining the patient's magnetic resonance imaging (MRI) dataof the knee showing at least the cartilage on at least one side of thejoint,

[0488] (b) segmenting the MRI data from step (a),

[0489] (c) generating a geometrical or biochemical representation of thecartilage of the joint from the segmented MRI data,

[0490] (d) assessing the patient's gait to determine the cartilagesurface contact pattern in the joint during the gait assessment, and

[0491] (e) correlating the contact pattern obtained in step (d) with thegeometrical representation obtained in step (c).

[0492] Still another aspect of this invention is a method for assessingthe condition of the knee joint of a human patient, wherein the kneejoint comprises cartilage and associated bones on either side of thejoint. The method comprises

[0493] (a) obtaining the patient's magnetic resonance imaging (MRI) dataof the knee showing at least the bones on either side of the joint,

[0494] (b) segmenting the MRI data from step (a),

[0495] (c) generating a geometrical representation of the bone of thejoint from the segmented MRI data,

[0496] (d) assessing the patient's gait to determine the load pattern ofthe articular cartilage in the joint during the gait assessment,

[0497] (e) correlating the load pattern obtained in step (d) with thegeometrical representation obtained in step (c).

[0498] Another aspect of this invention is a method for deriving themotion of bones about a joint from markers placed on the skin, whichmethod comprises

[0499] (a) placing at least three external markers on the patient's limbsegments surrounding the joint,

[0500] (b) registering the location of each marker on the patient's limbwhile the patient is standing completing still and while moving thelimb,

[0501] (c) calculating the principal axis, principal moments anddeformation of rigidity of the cluster of markers, and

[0502] (d) calculating a correction to the artifact induced by themotion of the skin markers relative to the underlying bone.

[0503] Another aspect of the invention is a system for assessing thecondition of cartilage in a joint of a human, which system comprises

[0504] (a) a device for electronically transferring a cartilagedegeneration pattern for the joint to receiving device located distantfrom the transferring device;

[0505] (b) a device for receiving the cartilage degeneration pattern atthe remote location;

[0506] (c) a database accessible at the remote location for generating amovement pattern for the joint of the human wherein the databaseincludes a collection of movement patterns for human joints, whichpatterns are organized and can be accessed by reference tocharacteristics such as type of joint, gender, age, height, weight, bonesize, type of movement, and distance of movement;

[0507] (d) a device for generating a movement pattern that most closelyapproximates a movement pattern for the human patient based on thecharacteristics of the human patient;

[0508] (e) a device for correlating the movement pattern with thecartilage degeneration pattern; and

[0509] (f) a device for transmitting the correlated movement patternwith the cartilage degeneration pattern back to the source of thecartilage degeneration pattern.

[0510] In each of these aspects of the invention it is to be understoodthat a cartilage degeneration pattern may be, i.a., 2D or 3D thicknessmap of the cartilage or a biochemical map of the cartilage.

[0511] All publications and patent applications mentioned in thisspecification are herein incorporated by reference to the same extent asif each individual publication or patent application was. specificallyand individually indicated to be incorporated by reference.

[0512] The invention now being fully described, it will be apparent toone of ordinary skill in the art that many changes and modifications canbe made thereto without departing from the spirit or scope of theappended claims.

What is claimed is:
 1. A method of treating a human with a joint diseaseinvolving cartilage, which method comprises: obtaining an electronicimage of said joint, wherein said image includes both normal anddiseased cartilage tissue; electronically evaluating said image toobtain information comprising volume, area, thickness, curvature,geometry, biochemical contents, signal intensity or relaxation time ofsaid normal or diseased tissue; and selecting a therapy based on saidinformation.
 2. The method of claim 1, wherein said electronicallyevaluating comprises a method selected from the group consisting of: (1)a method of estimating the loss of cartilage in a joint, wherein thejoint comprises cartilage and accompanying bones on either side of thejoint, which method comprises obtaining a three-dimensional map of thecartilage at an initial time and calculating the thickness or regionalvolume of a region of degenerated cartilage so mapped at the initialtime, obtaining a three-dimensional map of the cartilage at a latertime, and calculating the thickness or regional volume of a region ofdegenerated cartilage so mapped at the later time, and determining theloss in thickness or regional volume of the region of degeneratedcartilage between the later and initial times; (2) a method forassessing the condition of cartilage in a joint of a human, which methodcomprises electronically transferring an electronically generated imageof a cartilage of the joint from a transferring device to a receivingdevice located distant from the transferring device; receiving thetransferred image at the distant location; converting the transferredimage to a degeneration pattern of the cartilage; and transmitting thedegeneration pattern to a site for analysis; (3) a method fordetermining the volume of cartilage loss in a region of a cartilagedefect of a cartilage in a joint of a mammal which method comprisesdetermining the thickness, D_(N), of the normal cartilage near thecartilage defect; obtaining the thickness of the cartilage defect,D_(D), of the region; subtracting D_(D) from D_(N) to give the thicknessof the cartilage loss, D_(L); and multiplying the D_(L) value times thearea of the cartilage defect, A_(D), to give the volume of cartilageloss; (4) a method of estimating the change of cartilage in a joint of amammal over time, which method comprises estimating the thickness orwidth or area or volume of a region of cartilage at an initial time T₁;estimating the thickness or width or area or volume of the region ofcartilage at a later time T₂; and determining the change in thethickness or width or area or volume of the region of cartilage betweenthe initial and the later times; (5) a method for providing abiochemically based map of joint cartilage of a mammal, wherein thejoint comprises cartilage and associated bones on either side of thejoint, which method comprises measuring a detectable biochemicalcomponent throughout the cartilage; determining the relative amounts ofthe biochemical component throughout the cartilage; mapping the amountsof the biochemical component in three dimensions through the cartilage;and determining the areas of abnormal joint cartilage by identifying theareas having altered amounts of the biochemical component present; (6) amethod of estimating the change of cartilage in a joint, wherein thejoint comprises articular cartilage, which method comprises defining a3D object coordinate system of the joint at an initial time, T₁;identifying a region of a cartilage defect within the 3D objectcoordinate system; defining a volume of interest around the region ofthe cartilage defect whereby the volume of interest is larger than theregion of cartilage defect, but does not encompass the entire articularcartilage; defining the 3D object coordinate system of the joint at asecond timepoint, T₂; placing the identically-sized volume of interestinto the 3D object coordinate system at timepoint T₂ using the objectcoordinates of the volume of interest at timepoint T₁; and measuring anydifferences in cartilage volume within the volume of interest betweentimepoints T₁ and T₂; and (7) a method for correlating cartilage imagedata, bone image data, and opto-electrical image data for the assessmentof the condition of a joint, which method comprises (a) obtaining thecartilage image data of the joint with a set of skin reference markersplaced externally near the joint; (b) obtaining the bone image data ofthe joint with a set of skin reference markers positioned in the samemanner as the markers in; (c) obtaining the opto-electrical image dataof the joint with a set of skin reference markers positioned in the samemanner as (a) and (b); and using the skin reference markers to correlatethe images obtained in (a), (b) and (c) with each other, wherein eachskin reference marker is detectable in the cartilage and bone data andthe opto-electrical data;
 3. The method of claim 1, wherein saidelectronic image provides information on the thickness, shape, orcurvature of said normal and said disease tissue or the location andsize of said diseased tissue.
 4. The method of claim 1, wherein saidtherapy comprises autologous chondrocyte transplantation, osteochondralallografting, osteochondral autografting, tibial corticotomy, femoral ortibial osteotomy.
 5. The method of claim 1, wherein said therapy usescartilage or bone tissue grown ex vivo, stem cells, an artificialnon-human material, an agent that stimulates repair of said diseasedtissue, or an agent that protects said diseased tissue and that protectsadjacent normal tissue.
 6. The method of claim 1, wherein saidinformation is used to determine the thickness or other geometricalfeature of a tissue transplant, a tissue graft, a tissue implant, atissue replacement material, a tissue scaffold, or a tissue regeneratingmaterial or tissue repair system.
 7. The method of claim 1, wherein saidimage is obtained using ultrasound, computed tomography, positronemission tomotraphy, a single photon emission computed tomography scan,or MRI.
 8. The method of claim 7, wherein said information is used togenerate a three-dimensional map of cartilage thickness or a physicalmodel of said normal or said diseased tissue or both.
 9. The method ofclaim 8, wherein said physical model is used to shape a tissuetransplant, a tissue graft, a tissue implant, a tissue replacementmaterial, a tissue scaffold or a tissue regenerating material or tissuerepair system.
 10. A method of treating a human with cartilagedegeneration in a joint, which method comprises: obtaining an electronicimage of said joint, wherein said image includes both and diseasedcartilage; electronically evaluating said image to obtain informationcomprising volume, area, thickness, geometry, biochemical contents orrelaxation time of said normal or diseased tissue; and selecting atherapy to treat or replace said degenerated cartilage, wherein saidinformation is used during selection of treatment or replacement therapyof said degenerated cartilage.
 11. The method of claim 10, wherein saidinformation includes thickness, shape, curvature, or location anddimensions of said normal or degenerated cartilage.
 12. The method ofclaim 10, wherein said technique to treat or replace said degeneratedcartilage is autologous chondrocyte transplantation, osteochondralallografting, osteochondral autografting, tibial corticotomy, or femoralor tibial osteotomy.
 13. The method of claim 10, wherein said treatmentor replacement therapy uses cartilage or bone tissue grown ex vivo, stemcells, an artificial non-human material, an agent that stimulates repairof said diseased tissue, or an agent that protects said diseased tissueand that protects adjacent normal tissue.
 14. The method of claim 10wherein said information is used to determine the thickness, shape,curvature, or location and dimensions of a cartilage transplant, acartilage graft, a cartilage implant, a cartilage replacement material,a scaffold for cartilage cells or acellular cartilage components or acartilage regenerating material or a cartilage repair system.
 15. Themethod of claim 10, wherein said image is obtained using ultrasound,computed tomography, positron emission tomography, a single photonemission computed tomography scan, or MRI.
 16. The method of claim 10,wherein said information is used to generate a three-dimensional map ofcartilage thickness or a physical model of said normal or said diseasedtissue or both.
 17. The method of claim 16, wherein said physical modelis used to shape a cartilage transplant, a cartilage graft, a cartilageimplant, a cartilage replacement material, a scaffold or a cartilageregenerating material or a cartilage repair system.
 18. The method ofclaim 16, wherein physical model comprises an area of diseased cartilageas well as adjacent normal tissue.
 19. The method of claim 18, whereinsaid adjacent normal tissue is bone, bone marrow, or normal cartilage.20. The method of claim 16, wherein said physical model is created withuse of a 3D Euclidian distance transformation.
 21. The method of claim16, wherein said physical model or a portion of said physical model isimplanted into a knee joint.
 22. The method of claim 16, wherein saidphysical model carries cartilage cells or cartilage matrix.
 23. A methodof treating a human with diseased cartilage in a joint, which methodcomprises: utilizing an MRI scan to generate a cross-sectionalelectronic image of said joint, wherein said image includes both normaland diseased cartilage; and utilizing information from said image tocreate a geometric model of an area of diseased cartilage, wherein saidgeometric model is used in selecting a treatment of said diseasedcartilage.
 24. The method of claim 23, wherein said area of diseasedcartilage includes adjacent normal tissue.
 25. The method of claim 23,wherein said geometric model is used to determine the shape of acartilage transplant, a cartilage graft, a cartilage implant, acartilage replacement material, a scaffold for cartilage cells oracellular cartilage components or a cartilage regenerating material or acartilage repair system.
 26. A method of assessing cartilage disease ina joint, wherein the joint comprises cartilage and accompanying bones oneither side of the joint, which method comprises: obtaining athree-dimensional map of the cartilage demonstrating the thickness orbiochemical contents or relaxation time of normal and diseasedcartilage; and determining the margins of the diseased cartilage inrelationship to the normal cartilage in said three-dimensional map. 27.The method of claim 26, wherein said determination of said margins ofsaid diseased cartilage is performed by detecting a difference in saidthickness, said biochemical contents or said relaxation time betweensaid normal and said diseased cartilage.
 28. The method of claim 26,wherein said determination of said margins of said diseased cartilage isused to determine the area, volume, or thickness of diseased cartilage.29. The method of claim 26, wherein said determination of said marginsof said diseased cartilage is used to determine the percentage of totalcartilage surface area in a joint or along an articular surfacerepresented by diseased cartilage or the percentage of weight-bearingsurface area in a joint represented by diseased cartilage.
 30. Themethod of claim 26, wherein steps (a) through (b) are carried out at aninitial time (T₁) and are carried out again at a later time (T₂). 31.The method of claim 30, wherein the estimation includes an analysis ofthe degree of degeneration of the cartilage between T₁ and T₂.
 32. Themethod of claim 26, wherein an MRI technique first obtains a series oftwo-dimensional views of the joint, which are then mathematicallyintegrated to give a three-dimensional image.
 33. The method of claim32, wherein the MRI technique employs a gradient echo, spin echo,fast-spin echo, driven equilibrium Fourier transform, or spoiledgradient echo technique.